Search results for: Shougi Suliman Abosuliman

Authors: Shougi Suliman Abosuliman, Arun Kumar, Firoz Alam

Abstract:

Disaster preparedness is a key success factor for any effective disaster management practices. This paper evaluates the disaster preparedness and management in Saudi Arabia using an empirical investigation approach. It presents the results of the survey conducted by interviewing representatives of the Saudi decision-makers and administrators responsible for disaster control in Jeddah before, during and after flooding in 2009 and 2010. First, demographics of the respondents are presented, followed by quantitative analysis of their views and experiences regarding the Kingdom’s readiness before and after each flood. This is shown as a series of dependent and independent variables. Following this is a list of respondents’ priorities for disaster preparation in the Kingdom.

Keywords: Disaster response policy, crisis management, effective service delivery.

Authors: Mohamed Idris S. Abozaed, Suliman Mohamed Elrajoubi

Abstract:

Researches and concerns in power quality gained significant momentum in the field of power electronics systems over the last two decades globally. This sudden increase in the number of concerns over power quality problems is a result of the huge increase in the use of non-linear loads. In this paper, power quality evaluation of some distribution networks at Misurata - Libya has been done using a power quality and energy analyzer (Fluke 437 Series II). The results of this evaluation are used to minimize the problems of power quality. The analysis shows the main power quality problems that exist and the level of awareness of power quality issues with the aim of generating a start point which can be used as guidelines for researchers and end users in the field of power systems.

Keywords: Power Quality Disturbances, Power Quality Evaluation, Statistical Analysis.

Authors: Adil Elrayah, Jie Weng, Esra Suliman

Abstract:

The mineral in human bone is not pure stoichiometric calcium phosphate (Ca/P) as it is partially substituted by in organic elements. In this study, the copper ions (Cu²⁺) substituted hydroxyapatite (CuHA) powder has been synthesized by the co-precipitation method. The CuHA powder has been used to fabricate CuHA fiber scaffolds by sol-gel process and the following sinter process. The resulted CuHA fibers have slightly different microstructure (i.e. porosity) compared to HA fiber scaffold, which is denser. The mechanical properties test was used to evaluate CuHA, and the results showed decreases in both compression strength and hardness tests. Moreover, the in vitro used endothelial cells to evaluate the angiogenesis of CuHA. The result illustrated that the viability of endothelial cell on CuHA fiber scaffold surfaces tends to antigenic behavior. The results obtained with CuHA scaffold give this material benefit in biological applications such as antimicrobial, antitumor, antigens, compacts, filling cavities of the tooth and for the deposition of metal implants anti-tumor, anti-cancer, bone filler, and scaffold.

Keywords: Fiber scaffold, copper ions, hydroxyapatite, hardness, in vitro, mechanical properties.

Authors: Jameela Ali Alkrimi, Loay E. George, Azizah Suliman, Abdul Rahim Ahmad, Karim Al-Jashamy

Abstract:

Red blood cells (RBCs) are among the most commonly and intensively studied type of blood cells in cell biology. Anemia is a lack of RBCs is characterized by its level compared to the normal hemoglobin level. In this study, a system based image processing methodology was developed to localize and extract RBCs from microscopic images. Also, the machine learning approach is adopted to classify the localized anemic RBCs images. Several textural and geometrical features are calculated for each extracted RBCs. The training set of features was analyzed using principal component analysis (PCA). With the proposed method, RBCs were isolated in 4.3secondsfrom an image containing 18 to 27 cells. The reasons behind using PCA are its low computation complexity and suitability to find the most discriminating features which can lead to accurate classification decisions. Our classifier algorithm yielded accuracy rates of 100%, 99.99%, and 96.50% for K-nearest neighbor (K-NN) algorithm, support vector machine (SVM), and neural network RBFNN, respectively. Classification was evaluated in highly sensitivity, specificity, and kappa statistical parameters. In conclusion, the classification results were obtained within short time period, and the results became better when PCA was used.

Keywords: Red blood cells, pre-processing image algorithms, classification algorithms, principal component analysis PCA, confusion matrix, kappa statistical parameters, ROC.

Authors: Jameela Ali Alkrimi, Hamid A. Jalab, Loay E. George, Abdul Rahim Ahmad, Azizah Suliman, Karim Al-Jashamy

Abstract:

Red blood cells (RBC) are the most common types of blood cells and are the most intensively studied in cell biology. The lack of RBCs is a condition in which the amount of hemoglobin level is lower than normal and is referred to as “anemia”. Abnormalities in RBCs will affect the exchange of oxygen. This paper presents a comparative study for various techniques for classifying the RBCs as normal or abnormal (anemic) using WEKA. WEKA is an open source consists of different machine learning algorithms for data mining applications. The algorithms tested are Radial Basis Function neural network, Support vector machine, and K-Nearest Neighbors algorithm. Two sets of combined features were utilized for classification of blood cells images. The first set, exclusively consist of geometrical features, was used to identify whether the tested blood cell has a spherical shape or non-spherical cells. While the second set, consist mainly of textural features was used to recognize the types of the spherical cells. We have provided an evaluation based on applying these classification methods to our RBCs image dataset which were obtained from Serdang Hospital - Malaysia, and measuring the accuracy of test results. The best achieved classification rates are 97%, 98%, and 79% for Support vector machines, Radial Basis Function neural network, and K-Nearest Neighbors algorithm respectively.

Keywords: K-Nearest Neighbors, Neural Network, Radial Basis Function, Red blood cells, Support vector machine.