Ahmed Fahim

Work place: Faculty of Sciences and Humanitarian Study,Prince Sattam Bin Abdulaziz University, Al-Aflaj, KSA.

E-mail: a.abualeala@psau.edu.sa


Research Interests: Data Mining, Data Structures and Algorithms


Ahmed M. Fahim was born in December 1976, Menofia, Egypt, got his Bs. in computer science from Faculty of Science, Menofia University in 1998, and PhD. In 2010, and work at Faculty of computers and information, Suez University, Suez, Egypt. Now he is working at prince Sattam Bin Abdulaziz University, KSA. He is interested in data mining and knowledge discovery, and has published some research papers in different international journals and conferences.


Author Articles
Access Digital Divide in the Kingdom of Saudi Arabia (KSA): Current State and Measures for Improvement

By Altahir Saad Ahmed Fahim

DOI: https://doi.org/10.5815/ijieeb.2021.03.02, Pub. Date: 8 Jun. 2021

In this age the emergence of information and communication technologies (ICTs) has been identified as a major step toward solve the problems challenged the nation development. Problems such as corruption, delays in service delivery, lack of public sector accountability can be overcome with ICT. Furthermore, ICT are the key factors in improving government business and human sustainable development in all life aspects. Whilst the ICT considered the key to these problems but owning these technologies was facing many obstacles staring from bought them to continuous use, and create a gap between countries and within a country from the perspective of who does have computer and networks communication and who doesn't, and this refers to the digital divide. Some aspects of the digital divide exist everywhere and not only related to developing countries but also the size of the gap, which is different in countries and within a single community.
This study focuses on the digital divide problem by exploring the current state of the access digital divide in Kingdom of Saudi Arabia (KSA) based on three main research questions. And to achieve that, Data collected from International Telecommunication Union (ITU), Communications and Information Technology Commission (CITC), and World Bank were used.
The study found that Saudi Arabia is suffering from the access digital divide, and there is a strong link between household income and the access digital divide resulting from unaffordable prices in both ICT and broadband services and this gap tends to be larger in the regions where the inhabitants have the lowest income level.
The study recommends that the government should give improving household income the highest priorities and at the same time offering affordable prices for broadband services. Also, the study finds that mobile penetration represents a valuable resource for the Saudi Arabia government to be investing in delivering government services through mobile platforms. Finally, the study recommends that public-private partnerships with promoting and encouraging the private sectors to invest in ICT is one of the most important measurements in bridging the access digital divide.

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Finding the Number of Clusters in Data and Better Initial Centers for K-means Algorithm

By Ahmed Fahim

DOI: https://doi.org/10.5815/ijisa.2020.06.01, Pub. Date: 8 Dec. 2020

The k-means is the most well-known algorithm for data clustering in data mining. Its simplicity and speed of convergence to local minima are the most important advantages of it, in addition to its linear time complexity. The most important open problems in this algorithm are the selection of initial centers and the determination of the exact number of clusters in advance. This paper proposes a solution for these two problems together; by adding a preprocess step to get the expected number of clusters in data and better initial centers. There are many researches to solve each of these problems separately, but there is no research to solve both problems together. The preprocess step requires o(n log n); where n is size of the dataset. This preprocess step aims to get initial portioning of data without determining the number of clusters in advance, then computes the means of initial clusters. After that we apply k-means on original data using the resulting information from the preprocess step to get the final clusters. We use many benchmark datasets to test the proposed method. The experimental results show the efficiency of the proposed method.

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Homogeneous Densities Clustering Algorithm

By Ahmed Fahim

DOI: https://doi.org/10.5815/ijitcs.2018.10.01, Pub. Date: 8 Oct. 2018

Clustering based-density is very attractive research area in data clustering. DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm is the pioneer in this area. It can handle varied shaped and sized clusters. Also, it copes with noise and outliers efficiently, however it fails to handle clusters with varied densities due to the global parameter Eps. In this paper, we propose a method overcomes this problem, this method does not allow large variation in density within a cluster and use only two input parameters that will be called minpts and maxpts. They govern the minimum and maximum density of core objects within a cluster. The maxpts parameter will be used to control the value of Eps (neighborhood radius) in original DBSCAN. By allowing Eps to be varied from one cluster to another based on density of region this make DBSCAN able to handle varied density clusters and discover homogeneous clusters. The experimental results reflect the efficiency of the proposed method despite its simplicity.

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A Clustering Algorithm based on Local Density of Points

By Ahmed Fahim

DOI: https://doi.org/10.5815/ijmecs.2017.12.02, Pub. Date: 8 Dec. 2017

Data clustering is very active and attractive research area in data mining; there are dozens of clustering algorithms that have been published. Any clustering algorithm aims to classify data points according to some criteria. DBSCAN is the most famous and well-studied algorithm. Clusters are recorded as dense regions separated from each other by spars regions. It is based on enumerating the points in Eps-neighborhood of each point. This paper proposes a clustering method based on k-nearest neighbors and local density of objects in data; that is computed as the total of distances to the most near points that affected on it. Cluster is defined as a continuous region that has points within local densities fall between minimum local density and maximum local density. The proposed method identifies clusters of different shapes, sizes, and densities. It requires only three parameters; these parameters take only integer values. So it is easy to determine. The experimental results demonstrate the superior of the proposed method in identifying varied density clusters.

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