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).
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Cancer disease has a complicated pathophysiology and is one of the major causes of death and morbidity. Classical cancer therapies include chemotherapy, radiation therapy, and immunotherapy. A typical treatment is chemotherapy, which delivers cytotoxic medications to patients to suppress the uncontrolled growth of cancerous cells. Conventional oral medication has a number of drawbacks, including a lack of selectivity, cytotoxicity, and multi-drug resistance, all of which offer significant obstacles to effective cancer treatment. Multidrug resistance (MDR) remains a major challenge for effective cancer chemotherapeutic interventions. The advent of nanotechnology approach has developed the field of tumor diagnosis and treatment. Cancer nanote
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The fuzzy assignment models (FAMs) have been explored by various literature to access classical values, which are more precise in our real-life accomplishment. The novelty of this paper contributed positively to a unique application of pentagonal fuzzy numbers for the evaluation of FAMs. The new method namely Pascal's triangle graded mean (PT-GM) has presented a new algorithm in accessing the critical path to solve the assignment problems (AP) based on the fuzzy objective function of minimising total cost. The results obtained have been compared to the existing methods such as, the centroid formula (CF) and centroid formula integration (CFI). It has been demonstrated that operational efficiency of this conducted method is exquisitely develo
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Each organization struggles to exploit each possible opportunity for gaining success and continuing with its work carrier. In this field, organization success can be concluded by fulfilling end user requirements combined with optimizing available resources usage within a specified time and acceptable quality level to gain maximum profit. The project ranking process is governed by the multi-criteria environment, which is more difficult for the governmental organization because other organizations' main target is maximizing profit constrained with available resources. The governmental organization should consider human, social, economic and many more factors. This paper focused on building a multi-criteria optimizing proje
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In this work, copper substituted cobalt ferrite nanoparticles with
chemical formula Co1-xCuxFe2O4 (x=0, 0.3, and 0.7), has been
synthesized via hydrothermal preparation method. The structure of
the prepared materials was characterized by X-ray diffraction (XRD).
The (XRD) patterns showed single phase spinel ferrite structure.
Average crystallite size (D), lattice constant (a), and crystal density
(dx) have been calculated from the most intense peak (311).
Comparative standardization also performed using smaller average
particle size (D) on the XRD patterns of as-prepared ferrite samples
in order to select most convenient hydrothermal synthesis conditions
to get ferrite materials with smallest average particl
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Potential data interpretation is significant for subsurface structure characterization. The current study is an attempt to explore the magnetic low lying between Najaf and Diwaniyah Cities, In central Iraq. It aims to understand the subsurface structures that may result from this anomaly and submit a better subsurface structural image of the region. The study area is situated in the transition zone, known as the Abu Jir Fault Zone. This tectonic boundary is an inherited basement weak zone extending towards the NW-SE direction. Gravity and magnetic data processing and enhancement techniques; Total Horizontal Gradient, Tilt Angle, Fast Sigmoid Edge Detection, Improved Logistic, and Theta Map filters highlight source boundaries and the
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