Crime is considered as an unlawful activity of all kinds and it is punished by law. Crimes have an impact on a society's quality of life and economic development. With a large rise in crime globally, there is a necessity to analyze crime data to bring down the rate of crime. This encourages the police and people to occupy the required measures and more effectively restricting the crimes. The purpose of this research is to develop predictive models that can aid in crime pattern analysis and thus support the Boston department's crime prevention efforts. The geographical location factor has been adopted in our model, and this is due to its being an influential factor in several situations, whether it is traveling to a specific area or livin

Facial emotion recognition finds many real applications in the daily life like human robot interaction, eLearning, healthcare, customer services etc. The task of facial emotion recognition is not easy due to the difficulty in determining the effective feature set that can recognize the emotion conveyed within the facial expression accurately. Graph mining techniques are exploited in this paper to solve facial emotion recognition problem. After determining positions of facial landmarks in face region, twelve different graphs are constructed using four facial components to serve as a source for sub-graphs mining stage using gSpan algorithm. In each group, the discriminative set of sub-graphs are selected and fed to Deep Belief Network (DBN) f

Face recognition is required in various applications, and major progress has been witnessed in this area. Many face recognition algorithms have been proposed thus far; however, achieving high recognition accuracy and low execution time remains a challenge. In this work, a new scheme for face recognition is presented using hybrid orthogonal polynomials to extract features. The embedded image kernel technique is used to decrease the complexity of feature extraction, then a support vector machine is adopted to classify these features. Moreover, a fast-overlapping block processing algorithm for feature extraction is used to reduce the computation time. Extensive evaluation of the proposed method was carried out on two different face ima

The research aims to determine the impact of employees’ retention strategy on organizational memory. This research is historical, descriptive, and analytical. The sample consists of 158 faculty members in five private colleges in Baghdad. The technique used to analyze the data is SEM (Structural Equation Modeling), and SPSS (Statistical Package for the Social Sciences). The research concludes that the employees retaining strategy plays a vital role in retaining employees and hence maintains organizational memory. The findings and recommendations of this research assure the administrations of private colleges that employees retention strategy play a vital role in retaining its employee and hence maintains organizational memory. T

This research is carried out to investigate the behavior of self-compacting concrete (SCC) two-way slabs with central square opening under uniformly distributed loads. The experimental part of this research is based on casting and testing six SCC simply supported square slabs having the same dimentions and reinforcement. One of these slabs was cast without opening as a control slab. While, the other five slabs having opening ratios (O_R) of 2.78%, 6.25%, 11.11%, 17.36% and 25.00%. From the experimental results it is found that the maximum percentage decrease in cracking and ultimate uniform loads were 31.82% and 12.17% compared to control slab for opening ratios (O_R

Background: It is important to achieve good glycemic control to avoid long-term diabetic complications. It has been largely debated about the role of correct way of insulin administration to get the desired glycemic control.
Objective: To evaluate the effect of teaching diabetic patients who are on insulin therapy the correct way of injecting insulin and its effect on glycemic control.
Methods: A non randomized clinical trial with 820 diabetic patients on insulin therapy on whom A1 c estimation was performed before and after three months of teaching them the right injection technique.
Results : Sixty seven patients (8.17%) had A1 c 6.5% before they were enrolled in the study while the majority (753 patents, 91.82%) had A1 c 6.5%

Background: It is important to achieve good glycemic control to avoid long-term diabetic complications. It has been largely debated about the role of correct way of insulin administration to get the desired glycemic control.
Objective: To evaluate the effect of teaching diabetic patients who are on insulin therapy the correct way of injecting insulin and its effect on glycemic control.
Methods: A non randomized clinical trial with 820 diabetic patients on insulin therapy on whom A1 c estimation was performed before and after three months of teaching them the right injection technique.
Results : Sixty seven patients (8.17%) had A1 c 6.5% before they were enrolled in the study while the majority (753 patents, 91.82%) had A1 c 6.5%

Recommender Systems are tools to understand the huge amount of data available in the internet world. Collaborative filtering (CF) is one of the most knowledge discovery methods used positively in recommendation system. Memory collaborative filtering emphasizes on using facts about present users to predict new things for the target user. Similarity measures are the core operations in collaborative filtering and the prediction accuracy is mostly dependent on similarity calculations. In this study, a combination of weighted parameters and traditional similarity measures are conducted to calculate relationship among users over Movie Lens data set rating matrix. The advantages and disadvantages of each measure are spotted. From the study, a n

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