Automatic Cover Letter Generator System from CVs

The proposed system comes to overcome the problem of writing a C.V. Cover letter which requires some linguistic skills and a lot of experience in this domain in addition to its cost in term of time and money. The ACLGS solved the problem by developing an auto generated cover letter based on the user C.V. regardless its format. The ACLGS takes the user C.V. and the carrier announcement that contains the job requirements and the skills needed as input. The system solved the problem by building a template as a frame of slots each slot contains a required skill for the job; the system extracted the required information from the user CV and fills the slots in an automatic fashion. The ACLGS applies the Information retrieval methodologies to extract information with intelligence trends to mine the user C.V. in terms of part of speech tags and some of indicator words that the system used to recognize the proper data and required information. In addition, the system specifies a set of features for each slot in the form. The user C.V. clustered into a number of categories (e.g. Personal information, Qualifications, Experience, Skill, Rewords, and Publications). These categories are used as additional features for the extracted information and data. The system took into account the problem of sentence coherence and improves the output document through using pre-specified sentences that inserted into the output document based on the extracted information discovered from the user C.V

Evaluation of COVID-19 Vaccine Refusal among AOU Students in Kuwait and their Families and their Expected Inclination Towards the Acceptance or Refusal of the Vaccine

The purpose of this research was to determine the factors influencing the refusal of a coronavirus disease (COVID- 19) vaccine among adult students from Arab Open University in Kuwait (AOU) and their families and to study the trends of reluctant participants. A questionnaire was conducted (n = 691; aged 12 and older). Significant factors and the tendency of hesitant participants to accept or reject the vaccine were explored by applying a cleaning and coding process, a rough set theory (RS), a decision tree (DT) classifier, and a p-value. Overall, 18.4% of the participants reported refusing to receive a COVID-19 vaccine, while 17.2% were uncertain. The study shows that hesitant subjects represent a tendency to accept vaccination. Of the vaccine-refusal participants, subjects aged 18-29, suffer from chronic disease, were infected with COVID-19, were vaccinated against seasonal flu, and had concerns about receiving a COVID-19, representing 44.1%, 21.05%, 16.76%, 54.33%, and 70.08%, respectively. Overall, 18.4% of the participants demonstrated a refusal to receive a COVID-19 vaccine and 17.2% are hesitant. Factors influencing the level of acceptance/rejection of the vaccine were determined. The results showed that hesitant participants have a strong tendency to accept the vaccine (81.82%). Since vaccination is an important strategy to reduce the spread of the COVID-19 pandemic, the ministry of public health must immediately address the significant factors for the acceptance/rejection of the vaccine, as well as the trend of hesitant participants toward the acceptance of the vaccine

Prediction And Simulation Of Spatial Pattern For Urban Growth And Change In Land Use In Sana’a City, Yemen

In this study, Sana’a master plans were evaluated and analyzed to verify whether their implementations corresponded with the actual spatial urban development. The result shows that until the present time there is still lack of clear policy that controls and guides urban development. It also shows that about 40% of the growth occurred in unplanned areas, green areas and reserved land without suitable protection and regulations. GIS, remote sensing techniques and field survey were used to study the spatial pattern growth for the spontaneous areas in Sana’a city as well as the physical, socio-economic, and environmental conditions. There is no specific planning pattern was found in these settlements. Development has taken place randomly in unplanned areas, following the pattern of topography and concentrating along main roads. The study has successfully developed a model for locating suitable land for urban development in Sana’a by integrating GIS and Multi-criteria Analysis and Cellular Automata methods. The potential suitable lands were generated and the validation of the model was done by overlaying the generated suitability map on the potential land for residential development proposed by 1999 Sana’a master plan. The result shows the areas for future development proposed by the master plan corresponded well with the high to very high suitability zones except for illegal areas. The prediction and simulation of the urban growth and land use change were done successfully in GIS-based CA model which output “managed growth scenario”. Based on the land suitability assessment produced by the model, the demand for land for urban development during the period from 2004 to 2020 was then estimated using statistical tools. Then, the candidature of a cell by adopting again MCA method was evaluated. It provides dynamic transition rule for land use conversion at each time step of the simulation model based on the following factors used: land suitability, proximity to existing developed areas, proximity to prioritized land, and current land use. Variable calculation produces land use conversion probabilities for each cell. The rules are updated at each time step in order to reflect the land dynamics of the previous step. The result was validated through the process of running the model for the period from 1994 to 2003. The result gives an overall accuracy of 99.6%, producer’s accuracy of 83.3% and the user’s accuracy of 83.6%. In this study the SLEUTH model was also used to predict the urban growth and land use change. It was calibrated using 35-year time series dataset compiled from interpreted historical topographical maps, aerial photographs and satellite imageries for the entire study area to identify the parameters that influenced the urban growth in Sana’a city. Results from the calibration modes- coarse, fine, and final represented the top five scorings from thousands of iterations. The composite results of the optimum values for the diffusion, spread, slope and road gravity parameters show successive improvements in the parameters that control the behavior of the system. In the mechanism of self-modification rules, parameters averaging on the best results from the final calibration were used. The prediction mode of the SLEUTH model uses the best fit growth rule parameters from the calibration to begin the process of ‘‘growing’’ urban settlements, starting at the most recent urban data layer. The resulting forecast of future urban growth outputs a probability map where individual grid cells are being urbanized at some future date, assuming the same unique ‘‘urban growth signature’’ is still in effect as it was in the past. The final results of the model are annual layers map of future urban growth and land use change (2004–2020). Based on the analysis the comparison between GIS-based CA model and SLEUTH model carried out and the strong and weak points of them were highlighted. This study benefits decision makers and planners in carrying out future urban growth planning and it gives them the opportunity to know the advantages and consequences for each growth scenario in order to promote the continuity and sustainability of urban development in the Sana’a city

Dai’Shi (ISIS) Discourse in Arab Societies and the Emigration of Eastern Christians

Arab societies are suffering, since the events of the so-called “Arab Spring” 2011, a condition of conflict, terrorism, and instability. All the foregoing has produced a state of extremism towards the Christians of the Arab East as an essential component of Arab societies, thereby causing their exodus outside their countries, whether Iraq or Syria. The question presents itself: Are the events of the Arab Spring responsible for the exodus of the Christians of the East? Or is the matter related to the religious discourse and the school curricula which inculcate in the minds of students and youth what makes them reach this state of violence and extremism towards the other?The study has concluded that extremism and violence are a direct consequence of the official discourse, whether in the mosque, or school, alongside encouragement or at least a tacit acceptance by the Arab political regimes of those forms of conduct which may be utilized as a “religious card” to obtain internal political legitimacy on the one hand, while highlighting their role as a “protector” of religious and ethnic minorities on the other

New Learning Models for Generating Classification Rules Based on Rough Set Approach

Data sets, static or dynamic, are very important and useful for presenting real life features in different aspects of industry, medicine, economy, and others. Recently, different models were used to generate knowledge from vague and uncertain data sets such as induction decision tree, neural network, fuzzy logic, genetic algorithm, rough set theory, and others. All of these models take long time to learn for a huge and dynamic data set. Thus, the challenge is how to develop an efficient model that can decrease the learning time without affecting the quality of the generated classification rules. Huge information systems or data sets usually have some missing values due to unavailable data that affect the quality of the generated classification rules. Missing values lead to the difficulty of extracting useful information from that data set. Another challenge is how to solve the problem of missing data. Rough set theory is a new mathematical tool to deal with vagueness and uncertainty. It is a useful approach for uncovering classificatory knowledge and building a classification rules. So, the application of the theory as part of the learning models was proposed in this thesis. Two different models for learning in data sets were proposed based on two different reduction algorithms. The split-condition-merge-reduct algorithm ( SCMR) was performed on three different modules: partitioning the data set vertically into subsets, applying rough set concepts of reduction to each subset, and merging the reducts of all subsets to form the best reduct. The enhanced-split-condition-merge-reduct algorithm (E SCMR) was performed on the above three modules followed by another module that applies the rough set reduction concept again to the reduct generated by SCMR in order to generate the best reduct, which plays the same role as if all attributes in this subset existed. Classification rules were generated based on the best reduct. For the problem of missing data, a new approach was proposed based on data partitioning and function mode. In this new approach, the data set was partitioned horizontally into different subsets. All objects in each subset of data were described by only one classification value. The mode function was applied to each subset of data that has missing values in order to find the most frequently occurring value in each attribute. Missing values in that attribute were replaced by the mode value. The proposed approach for missing values produced better results compared to other approaches. Also, the proposed models for learning in data sets generated the classification rules faster than other methods. The accuracy of the classification rules by the proposed models was high compared to other models

A WGFS Based Approach to Extract Factors Influencing the Marketing of Korean Language in GCC

This research proposed an approach that is intended to determine the minimal set of important factors that influence the desire of learning Korean language in the Gulf Cooperation Council (GCC). Those factors will then influence marketing of the Korean language in GCC by guiding interested people to increase their commercial abilities, improve their information about Korean drama, and prepare them to study or travel to Korea. A total of 500 responses out of 526 questionnaires were used for the analysis process. Merging the weight by SVM and the weight guided feature selection (WGFS) techniques were proposed to build a strong hybrid model of reduction for the investigated dataset. Five different classifiers were used to test the results. Empirical results have showed that the generated factors (the reduct) are very significant to test the ability/inability of learning the Korean language. SVM was shown as the best with accuracy value of 94%. This research contributed to the literature by highlighting the importance of the Korean language in the GCC and by presenting the important factors that influence learners of the Korean language: encouragements and obstacles. Moreover, current research presented the best classifier which yields to the high performance of classification

An Efficient Approach towards Network Routing using Genetic Algorithm

The network field has been very popular in recent times and has aroused much of the attention of researchers. The network must keep working with the varying infrastructure and must adapt to rapid topology changes. Graphical representation of the networks with a series of edges varying over time can help in analysis and study. This paper presents a novel adaptive and dynamic network routing algorithm based on a Regenerate Genetic Algorithm (RGA) with the analysis of network delays. With the help of RGA at least a very good path, if not the shortest one, can be found starting from the origin and leading to a destination. Many algorithms are devised to solve the shortest path (SP) problem for example Dijkstra algorithm which can solve polynomial SP problems. These are equally effective in wired as well as wireless networks with fixed infrastructure. But the same algorithms offer exponential computational complexity in dealing with the real-time communication for rapidly changing network topologies. The proposed genetic algorithm (GA) provides more efficient and dynamic solutions despite changes in network topology, network change, link or node deletion from the network, and the network volume (with numerous routes)

Solvent Induced Oil Viscosity Reduction and Its Effect on Waterflood Recovery Efficiency

WAG process is one of the techniques used for reducing gas consumption, enhancing recovery factor and achieving better profile control of displacing fluids. Recovery efficiency due to reduction of oil viscosity, simulating a WAG process, in a wide range of reservoir permeability and water injection rate was investigated. Gas viscosity reduction by miscible gas or solvent injection is mimicked by progressive dilution of a medium density crude oil with a mixture of hydrocarbon solvent. The porous media used in this study consists of a set of water wet sandstone core plugs of low to medium permeability. The experimental findings  show that reduced oil viscosity has no correlation with recovery efficiency, in the normal flood velocity regime. However, in the higher flood velocity regime, recovery efficiency reduces with increasing oil viscosity, only for higher permeability cores, which is attributed to micro-heterogeneity within pore geometry. The study suggests that the additional oil recovery during miscible gas injection, is mainly contributed by the swelling factor of oil which results in increased oil saturation, higher reservoir pressure and increased relative permeability of oil in addition to the contribution from lower interfacial tension and very little, if any due to oil viscosity reduction. Key words: WAG process; Recovery efficiency; Oil viscosit