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Visual Perception of Population Maps of Baghdad Governorate (Comparative Study Between Cartographic Representation Using Graphical Methods within the ARC & GIS)
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This paper will juxtapose the effectiveness of conventional methods of graphical cartography and GIS-interpolation methods in presenting the population distribution of the Baghdad Governorate in 2023. The study touches upon the efficiency of two types of cartographic techniques to present demographic data and is concerned with the speed and clearness of the visual perception. The analysis involves the use of traditional techniques (square, triangle, column and divided circle) and the GIS techniques (Kernel Smoothing, Kriging and Inverse Distance weighting) to describe the male, female and aggregate population. Fifty respondents used their comprehension speed on the maps as the sample. The findings demonstrate that the traditional techniques, especially the square, triangle and the divided circle, gave faster understanding and they scored 9/10 within 2 seconds. Conversely, GIS-based techniques, including Kernel Smoothing, depicted a sharp underperformance in visual perception, with a score of 2/10 in 20 seconds only. These GIS techniques though accurate in spatial interpolation, were not very clear especially at the district level hence limiting their application in urban planning. The research also brings out the issue of the difficulty in representing both sexes in one map through the use of GIS. Based on the results, traditional techniques are more effective in visualizing demographics because they are clearer and faster, particularly in the setting of urban planning, where fast decision-making is paramount. There is need to conduct further research to refine GIS techniques so as to enhance visual perception and district-accurate which will be more beneficial to urban planners and resource managers.

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Publication Date
Fri May 01 2026
Journal Name
2026 Xxix International Conference On Soft Computing And Measurements (scm)
Hierarchical Multi-Stage Intrusion Detection with Feature Inheritance and Prediction Verification
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One of the challenges faced by traditional intrusion detection systems based on machine learning or deep learning is instability when dealing with unbalanced network traffic, leading to failure in detecting certain attacks (minority classifications). Additionally, they struggle with multi-stage attacks, resulting in an increase in false alarms. This paper presents a hierarchical intrusion detection system supported by a Prediction Verification Layer (PVL) and a Feature Inheritance Mechanism (FIM). Where PVL contributes to documenting the system’s final decision and increasing sensitivity to minority attacks, FIM also helps in inheriting features from previous layers and correcting errors as much as possible. Additionally, it allows for ad

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Publication Date
Mon Mar 31 2025
Journal Name
Iraqi Statisticians Journal
Hypothesis Testing for Non-Normal Multiple Compact Regression Model
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Generalized multivariate transmuted Bessel distribution belongs to the family of probability distributions with a symmetric heavy tail. It is considered a mixed continuous probability distribution. It is the result of mixing the multivariate Gaussian mixture distribution with the generalized inverse normal distribution. On this basis, the paper will study a multiple compact regression model when the random error follows a generalized multivariate transmuted Bessel distribution. Assuming that the shape parameters are known, the parameters of the multiple compact regression model will be estimated using the maximum likelihood method and Bayesian approach depending on non-informative prior information. In addition, the Bayes factor was used

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Publication Date
Sat Oct 01 2016
Journal Name
Journal Of Theoretical And Applied Information Technology
Factors affecting global virtual teams’ performance in software projects
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