Three-dimensional (3D) image and medical image processing, which are considered big data analysis, have attracted significant attention during the last few years. To this end, efficient 3D object recognition techniques could be beneficial to such image and medical image processing. However, to date, most of the proposed methods for 3D object recognition experience major challenges in terms of high computational complexity. This is attributed to the fact that the computational complexity and execution time are increased when the dimensions of the object are increased, which is the case in 3D object recognition. Therefore, finding an efficient method for obtaining high recognition accuracy with low computational complexity is essential. To this end, this paper presents an efficient method for 3D object recognition with low computational complexity. Specifically, the proposed method uses a fast overlapped technique, which deals with higher-order polynomials and high-dimensional objects. The fast overlapped block-processing algorithm reduces the computational complexity of feature extraction. This paper also exploits Charlier polynomials and their moments along with support vector machine (SVM). The evaluation of the presented method is carried out using a well-known dataset, the McGill benchmark dataset. Besides, comparisons are performed with existing 3D object recognition methods. The results show that the proposed 3D object recognition approach achieves high recognition rates under different noisy environments. Furthermore, the results show that the presented method has the potential to mitigate noise distortion and outperforms existing methods in terms of computation time under noise-free and different noisy environments.
Mixture experiments are response variables based on the proportions of component for this mixture. In our research we will compare the scheffʼe model with the kronecker model for the mixture experiments, especially when the experimental area is restricted.
Because of the experience of the mixture of high correlation problem and the problem of multicollinearity between the explanatory variables, which has an effect on the calculation of the Fisher information matrix of the regression model.
to estimate the parameters of the mixture model, we used the (generalized inverse ) And the Stepwise Regression procedure
... Show MoreThis study aims at examining the effectiveness of using the narrative approach in teaching the Interpretation of the Qur'an course in the development of conceptual comprehension among first-grade middle school female students. To achieve the objective of this study, a quantitative quasi-experimental design has been used. The sample consisted of first-grade middle school female students at "the third middle school" in Buraidah city, as this school suits the objective of the study. A test of conceptual understanding has been built by the researchers according to a list of conceptual understating skills at a significance level of α ≤ 0.05. Results have shown that there are statistically significant differences at the level (α ≤ 0,05)
... Show MoreClimate change in recent years has greatly affected the distribution of ground covers. Monitoring these changes has become very easy due to the development of remote sensitivity science and the use of satellites to monitor these changes. The aim of this research is to monitor changes in the spectral reflectivity of the Baghdad governorate center for the month (March, June, September, December) of the year 2021 using remote sensing and satellite images Sentinel 2 and knowing the climate imact on them. Fifty-one samples were selected for four types of ground cover (agricultural land, water, buildings and open space) and their spectral reflectivity was calculated using satellite images.
In this study, a traumatic spinal cord injury (TSCI) classification system is proposed using a convolutional neural network (CNN) technique with automatically learned features from electromyography (EMG) signals for a non-human primate (NHP) model. A comparison between the proposed classification system and a classical classification method (k-nearest neighbors, kNN) is also presented. Developing such an NHP model with a suitable assessment tool (i.e., classifier) is a crucial step in detecting the effect of TSCI using EMG, which is expected to be essential in the evaluation of the efficacy of new TSCI treatments. Intramuscular EMG data were collected from an agonist/antagonist tail muscle pair for the pre- and post-spinal cord lesi
... Show Morenovel spectrofluorimetric flow injection analysis (FIA) method was developed for the selective quantification of ascorbic acid via fluorescence quenching of serotonin hydrochloride. The system employs a custom-designed photometric array comprising 16 irradiation sources arranged in a dual-axis matrix—eight aligned horizontally and eight orthogonally, enabling multi-angle excitation and enhanced spectral resolution. Fluorescence signals were captured using a twin-pair solar cell detector, offering high sensitivity and minimal optical interference. The method exhibited a linear calibration range of 0.1–30 limit of detection (LOD) of 0.025 μ μ g/mL with a correlation coefficient (r mol /L, equivalent to 4.403 * 10 4 μ 2 ) of 0.9966, a g
... Show MoreWater level and distribution is very essential in almost all life aspects. Natural and artificial lakes represent a large percentage of these water bodies in Iraq. In this research the changes in water levels are observed by calculating the areas of five different lakes in five different regions and two different marshes in two different regions of the country, in a period of 12 years (2001 - 2012), archived remotely sensed images were used to determine surface areas around lakes and marshes in Iraq for the chosen years . Level of the lakes corresponding to satellite determined surface areas were retrieved from remotely sensed data .These data were collected to give explanations on lake level and surface area fluctuations. It is imp
... Show MoreDespite the recognised importance of green onion as a nutritionally and medicinally valuable crop in Iraq, its production heavily depends on chemical fertilisers, which degrade soil fertility and environmental quality. Although organic manure, biofertilizers, and micronutrients such as boron individually improve soil health and crop performance, limited information is available on their integrated application under local agro-ecological conditions. In this regard, an experiment was conducted during the 2024-25 fall season at the fields of College of Agricultural Engineering Sciences, University of Baghdad, Iraq to study the effects of integrated nutrients managements on growth, yield and quality of bunching onion (Allium fistulosum)
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