Generally, radiologists analyse the Magnetic Resonance Imaging (MRI) by visual inspection to detect and identify the presence of tumour or abnormal tissue in brain MR images. The huge number of such MR images makes this visual interpretation process, not only laborious and expensive but often erroneous. Furthermore, the human eye and brain sensitivity to elucidate such images gets reduced with the increase of number of cases, especially when only some slices contain information of the affected area. Therefore, an automated system for the analysis and classification of MR images is mandatory. In this paper, we propose a new method for abnormality detection from T1-Weighted MRI of human head scans using three planes, including axial plane, coronal plane, and sagittal plane. Three different thresholds, which are based on texture features: mean, energy and entropy, are obtained automatically. This allowed to accurately separating the MRI slice into normal and abnormal one. However, the abnormality detection contained some normal blocks assigned wrongly as abnormal and vice versa. This problem is surmounted by applying the fine-tuning mechanism. Finally, the MRI slice abnormality detection is achieved by selecting the abnormal slices along its tumour region (Region of Interest-ROI).
The university course timetable problem (UCTP) is typically a combinatorial optimization problem. Manually achieving a useful timetable requires many days of effort, and the results are still unsatisfactory. unsatisfactory. Various states of art methods (heuristic, meta-heuristic) are used to satisfactorily solve UCTP. However, these approaches typically represent the instance-specific solutions. The hyper-heuristic framework adequately addresses this complex problem. This research proposed Particle Swarm Optimizer-based Hyper Heuristic (HH PSO) to solve UCTP efficiently. PSO is used as a higher-level method that selects low-level heuristics (LLH) sequence which further generates an optimal solution. The proposed a
... Show MoreThe effect of different Ti additions on the microstructure of Al-Ti alloy prepared by powder metallurgy was investigated. A certain amount of Ti (10wt%, 15wt%, and 20wt%) were added to aluminium and the tests like microhardness, density, scanning electron microscope (SEM), optical microscope (OM) and X-Ray Diffraction (XRD) were conducted to determine the influence of different Ti additives on the Al-Ti alloy properties and microstructure. The results show that the grains of α-Al changed from large grains to roughly spherical and then to small rounded grains with increasing Ti content, the micro-hardness of the alloy increases with increasing Ti, and XRD results confirm the formation of TiAl3 intermetallic co
... Show MoreBreast cancer (BC) is first of the top 10 malignancies in Iraq. Dose‐volume histograms (DVHs) are most commonly used as a plan evaluation tool. This study aimed to assess DVH statistics using three‐dimensional conformal radiotherapies in BC in an adjuvant setting.
A retrospective study of 70 histologically confirmed women diagnosed with BC was reviewed. The study was conducted between November 2020 and May 2021, planning for treatment with adjuvant three‐dimensional conformal radiotherapies. The treatment plan used for each woman was based on an analysis of the volumetric dose, inclu
Flow-production systems whose pieces are connected in a row may not have maintenance scheduling procedures fixed because problems occur at different times (electricity plants, cement plants, water desalination plants). Contemporary software and artificial intelligence (AI) technologies are used to fulfill the research objectives by developing a predictive maintenance program. The data of the fifth thermal unit of the power station for the electricity of Al Dora/Baghdad are used in this study. Three stages of research were conducted. First, missing data without temporal sequences were processed. The data were filled using time series hour after hour and the times were filled as system working hours, making the volume of the data relativel
... Show MoreThe measurement data of the raw water quality of Tigris River were statistically analyzed to measure the salinity value in relation to the selected raw water quality parameters. The analyzed data were collected from five water treatment plants (WTPs) assembled alongside of the Tigris River in Baghdad: Al-Karkh, Al-Karama, Al-Qadisiya, Al-Dora, and Al-Wihda for the period from 2015 to 2021. The selected parameters are total dissolved solid (TDS), electrical conductivity (EC), pH and temperature. The main objective of this research is to predicate a mathematical model using SPSS software to calculate the value of salinity along the river, in addition, the effect of electrical conductivi
Epithelial ovarian cancer is the leading cause of cancer deaths from gynecological malignancies. Angiogenesis is considered essential for tumor growth and the development of metastases. VEGF and IL?8 are potent angiostimulatory molecules and their expression has been demonstrated in many solid tumors, including ovarian cancer.VEGF and IL-8 concentrations were measured by ELISA test (HumanVEGF,IL-8). Bioassay ELISA/ US Biological / USA).The median VEGF and IL-8 levels were significantly higher in the sera of ovarian cancer patients than in those with benign tumors and in healthy controls.Pretreatment VEGF and IL-8 serum levels might be regarded as an additional tool in the differentiation of ovarian tumors.
A novel planar type antenna printed on a high permittivity Rogers’ substrate is proposed for early stage microwave breast cancer detection. The design is based on a p-shaped wide-slot structure with microstrip feeding circuit to eliminate losses of transmission. The design parameters are optimized resulting in a good reflection coefficient at −10 dB from 4.5 to 10.9 GHz. Imaging result using inhomogeneous breast phantom indicates that the proposed antenna is capable of detecting a 5 mm size cancerous tumor embedded inside the fibroglandular region with dielectric contrast between the target and the surrounding materials ranging from 1.7 : 1 to 3.6 : 1.
Sensibly highlighting the hidden structures of many real-world networks has attracted growing interest and triggered a vast array of techniques on what is called nowadays community detection (CD) problem. Non-deterministic metaheuristics are proved to competitively transcending the limits of the counterpart deterministic heuristics in solving community detection problem. Despite the increasing interest, most of the existing metaheuristic based community detection (MCD) algorithms reflect one traditional language. Generally, they tend to explicitly project some features of real communities into different definitions of single or multi-objective optimization functions. The design of other operators, however, remains canonical lacking any inte
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