Development of land value algorithm for establishing an effective cadastral system in Erbil City

Land value is one of the economic issues of cadastral systems which is the base of sustainable urban and regional planning. The current paper concerns the estimation of the land values according to many essential factors, which are adopted as ten variables generally. Among these ten parameters, the frontage of the parcel (width), the value of rent, the width of streets, and the level of services represent the most effective parameters that play the main role in process of land price estimation over the Erbil City. The current research introduces the nature of land values and their homogeneous distribution and evaluates the suggested algorithm of land price estimation as one of the efficient factors that affect the national economic situation. The data collection was done for 100 parcels in different locations within the Erbil city boundary, which is being selected to apply the linear multiple regression equation to find the coefficients of the effective factors and to define the correlation between them. The obtained results of the linear multiple regression equation show that the level of existing services and the value of the rent have the maximum effect among these four factors, and they have the maximum correlation with the land price, whereas the road’s width has the minimum correlation among them. The worked-out algorithm for land price estimation (which is a vital issue of the modern cadastral systems), is recommended to be applied by the institutions and organizations concerning the land prices and land taxes task

Using physiological MRI to estimate dynamic cerebral autoregulation metrics: functional MRI feasibility study

Cerebral autoregulation is the homeostatic mechanism that maintains sufficient cerebral circulation despite changes in the perfusion pressure. Dynamic CA refers to the changes that occur in CBF within the first few seconds after an acute MAP change. Assessment of the CA impairment plays important role in the prognosis of many cerebrovascular diseases such as stroke, sub-arachnoid haemorrhage, as well as traumatic brain injury and neurodegenerative disorders. This thesis investigates the feasibility of using physiological MRI to estimate dynamic cerebral autoregulation (dCA) metrics. In particular, this thesis has an emphasis on measuring beat-to-beat arterial blood pressure inside the scanner to provide better understanding of the physiological aspects of dCA. Further, continuous blood pressure (BP) measures in response to different non invasive BP fluctuating methods are acquired to evaluate the reliability of these methods to induce response changes. Blood Oxygen Level Dependent (BOLD) fMRI method was used to estimate the expected variations of tissue oxygenation during induced dCA changes in healthy volunteers. The non invasive arterial blood pressure measurements were acquired using MR compatible arterial blood pressure monitoring device (NIBP-MRI/Caretaker; Biopac®). Further, sudden release of inflated thigh-cuffs (TCR) and inspiratory breath-hold (iBH) methods were used in the scanner to induce dynamic autoregulatory changes. These two methods were investigated in a pilot study, to evaluate the reliability prior to the MR study by comparing BP measurements obtained outside the scanner using non invasive methods. This pilot study included monitoring BP changes in response to four types of non invasive BP fluctuating methods. The reliability of NIBP/MRI Caretaker device was examined by comparing the BP response changes with the simultaneously acquired BP data from Finometer plethysmographic device. The cerebral autoregulation metrics were estimated by calculating the rate of regulation (RoR) following dynamic BP fluctuating events. Rate of regulation defines the rate at which the BOLD signal changes depending on MAP changes at a particular time. Further, the tissue specific regulation parameters were obtained for grey matter (GM), white matter (WM) and water shed areas (WS). The effect of iBH method on cerebral blood flow (CBF) and velocity (CBFV) was explored in a preliminary study by quantitative measures using time resolved 4D PC MRI angiography in two subjects. The mean arterial blood pressure (MAP) changes in response to TCR and iBH method were comparable. The fMRI data demonstrated BOLD signal amplitude change in response to the induced fast MAP changes. The GM and WS areas showed similar rates of regulation, and these were nominally higher than WM RoR in both TCR and iBH methods. Further, the 4D PC MRI data suggested 29% CBF-increase in response to 33% iBH in four minutes acquisition time. The acquired non invasive arterial BP measures concurrent with the BOLD signal amplitude response, allowed deriving the rate of regulation as a metric of dCA. It is not known whether this information is clinically relevant to gauge the haemodynamic risk association to cerebrovascular disease. However, BOLD signal change and CBF changes after iBH are confounded by the extent to which the CO2 gradually accumulate in response to iBH and causes an overshoot in the CBF response-change. In conclusion, the presented study indicates the feasibility of using physiological MRI to measure dCA in response to non-invasively induced MAP changes. Estimation of the dCA metrics could be improved by using advanced data fitting methods as well as controlling for physiological parameters such as PECO2

Using physiological MRI to estimate dynamic cerebral autoregulation metrics: functional MRI feasibility study

Cerebral autoregulation is the homeostatic mechanism that maintains sufficient cerebral circulation despite changes in the perfusion pressure. Dynamic CA refers to the changes that occur in CBF within the first few seconds after an acute MAP change. Assessment of the CA impairment plays important role in the prognosis of many cerebrovascular diseases such as stroke, sub-arachnoid haemorrhage, as well as traumatic brain injury and neurodegenerative disorders. This thesis investigates the feasibility of using physiological MRI to estimate dynamic cerebral autoregulation (dCA) metrics. In particular, this thesis has an emphasis on measuring beat-to-beat arterial blood pressure inside the scanner to provide better understanding of the physiological aspects of dCA. Further, continuous blood pressure (BP) measures in response to different non invasive BP fluctuating methods are acquired to evaluate the reliability of these methods to induce response changes. Blood Oxygen Level Dependent (BOLD) fMRI method was used to estimate the expected variations of tissue oxygenation during induced dCA changes in healthy volunteers. The non invasive arterial blood pressure measurements were acquired using MR compatible arterial blood pressure monitoring device (NIBP-MRI/Caretaker; Biopac®). Further, sudden release of inflated thigh-cuffs (TCR) and inspiratory breath-hold (iBH) methods were used in the scanner to induce dynamic autoregulatory changes. These two methods were investigated in a pilot study, to evaluate the reliability prior to the MR study by comparing BP measurements obtained outside the scanner using non invasive methods. This pilot study included monitoring BP changes in response to four types of non invasive BP fluctuating methods. The reliability of NIBP/MRI Caretaker device was examined by comparing the BP response changes with the simultaneously acquired BP data from Finometer plethysmographic device. The cerebral autoregulation metrics were estimated by calculating the rate of regulation (RoR) following dynamic BP fluctuating events. Rate of regulation defines the rate at which the BOLD signal changes depending on MAP changes at a particular time. Further, the tissue specific regulation parameters were obtained for grey matter (GM), white matter (WM) and water shed areas (WS). The effect of iBH method on cerebral blood flow (CBF) and velocity (CBFV) was explored in a preliminary study by quantitative measures using time resolved 4D PC MRI angiography in two subjects. The mean arterial blood pressure (MAP) changes in response to TCR and iBH method were comparable. The fMRI data demonstrated BOLD signal amplitude change in response to the induced fast MAP changes. The GM and WS areas showed similar rates of regulation, and these were nominally higher than WM RoR in both TCR and iBH methods. Further, the 4D PC MRI data suggested 29% CBF-increase in response to 33% iBH in four minutes acquisition time. The acquired non invasive arterial BP measures concurrent with the BOLD signal amplitude response, allowed deriving the rate of regulation as a metric of dCA. It is not known whether this information is clinically relevant to gauge the haemodynamic risk association to cerebrovascular disease. However, BOLD signal change and CBF changes after iBH are confounded by the extent to which the CO2 gradually accumulate in response to iBH and causes an overshoot in the CBF response-change. In conclusion, the presented study indicates the feasibility of using physiological MRI to measure dCA in response to non-invasively induced MAP changes. Estimation of the dCA metrics could be improved by using advanced data fitting methods as well as controlling for physiological parameters such as PECO2

In vivo Sub-regional dGEMRIC Analysis and Contrast Distribution in Clinical Studies of Human Knee Cartilage

Aims: This work was carried out to investigate whether considering cartilage depth in vivo dGEMRIC would provide additional information on the molecular content and changes in normal and diseased cartilage. Methods: Study I was a longitudinal study on 23 healthy volunteers; Study II was a case-control study on 9 sedentary individuals and 8 elite runners. Study III was a longitudinal study on 30 patients with a history of medial meniscectomy. They were divided into three groups according to self-reported changes in level of physical activity. MRI measurements were performed in femoral knee cartilage pre- & post-injection of Gd-DTPA2-. Depth-wise times p.i. depending on the study group. Results: Studies I and II: before contrast injection, T1 was higher in the superficial region than in the deep regions of the cartilage. Bulk gadolinium concentration was negatively related to cartilage thickness. Gd-DTPA2- uptake was significantly slower in the deep region than in the superficial region of the cartilage. Gd concentration in the superficial region was independent of cartilage thickness. A trend was seen towards lower Gd concentration in the superficial layer of weight-bearing cartilage in elite runners, than in sedentary individuals. More contrast agent seen in superficial non-weight-bearing cartilage than weight-bearing cartilage. In Study III, those who increased their physical activity showed a significant increase in dGEMRIC index in both superficial and deep layers in lateral weight-bearing cartilage. Those who decreased their physical activity showed a significant decrease in dGEMRIC index in the medial weight-bearing cartilage. Conclusions: The higher pre-contrast T1 in the superficial region than in the deep region is an indication of a higher water content in superficial cartilage. The uptake of contrast agent was found to be from the superficial region of the cartilage, with diffusion into the deeper parts, and this affects the interpretation of bulk dGEMRIC measurements. The Gd concentration in the superficial layer supports dGEMRIC findings that cartilage has an adaptive capacity to exercise, leading to increased glycosaminoglycan (GAG) content throughout the cartilage. Decreasing physical activity leads to a decrease in GAG content in the cartilage. Variation in cartilage thickness is a source of error in dGEMRIC that should be considered when analysing bulk values

Detection of bone fracture based on machine learning techniques

Computers have been shown to be valuable in every facet of human life, from banking and online shopping to communication, education, research and development, and even medical. To help doctors and hospitals better care for their patients, a lot of innovative technical resources have been developed. Because the typical scanner for X-rays produces a fuzzy picture of the bone component in issue, surgeons risk making an inaccurate diagnosis of bone fractures when they utilize it. Various stages such as pre-processing, edge detection, feature extraction and machine learning classifications, constitute the backbone of this system, with the end goal of making surgeons' lives easier. Among the various fields that benefit from machine learning algorithms nowadays are seismology, remote sensing, and medicine; this program is only one example. As a side note, several machine learning algorithms, such as Naïve Bayes, Decision Tree, Nearest Neighbors, Random Forest, and SVM, have been used specifically for handling bone fracture detection on a dataset with 270 x-ray images. Accuracy measures for the various algorithms employed in the study range from 0.64 to 0.92, with values obtained for Naïve Bayes, Decision Tree, Nearest Neighbors, Random Forest, and SVM. Statistically, the accuracy for SVM was found to be the highest in this research, which is higher than most of the reviewed research

A comparative evaluation for Detection Brain Tumor in MRI Image using Machine learning algorithms

In medical imaging, automated defect identification of defects has taken on a prominent position. Unaided prediction of tumor (brain) recognition in magnetic resonance imaging process (MRI) is vital for patient preparation. With traditional methods of identifying z is designed to reduce the burden on radiologists. One of the problems with MRI brain tumor diagnosis is the size and variation of their molecular structures. This article uses deep learning techniques (Artificial neural network ANN, Naive Bayes NB, Multi-layer Perceptron MLP ) to discover brain tumors in the MRI scans. First, the brain MRI images are run through the preprocessing steps to remove texture features. Next, these features are used to train a machine learning algorithm

An Ensemble Transfer Learning Model for Detecting Stego Images

As internet traffic grows daily, so does the need to protect it. Network security protects data from unauthorized access and ensures their confidentiality and integrity. Steganography is the practice and study of concealing communications by inserting them into seemingly unrelated data streams (cover media). Investigating and adapting machine learning models in digital image steganalysis is becoming more popular. It has been demonstrated that steganography techniques used within such a framework perform more securely than do techniques using hand-crafted pieces. This work was carried out to investigate and examine machine learning methods’ critical contributions and beneficial roles. Machine learning is a field of artificial intelligence (AI) that provides the ability to learn without being explicitly programmed. Steganalysis is considered a classification problem that can be addressed by employing machine learning techniques and recent deep learning tools. The proposed ensemble model had four models (convolution neural networks (CNNs), Inception, AlexNet, and Resnet50), and after evaluating each model, the system voted on the best model for detecting stego images. Since active steganalysis is a classification problem that may be solved using active deep learning tools and modern machine learning methods, this paper’s major goal was to analyze deep learning algorithms’ vital roles and main contributions. The evaluation shows how to successfully detect images that contain a steganography algorithm that hides data in images. Thus, it suggests which algorithms work best, which need improvement, and which are easier to identify

Pre-contrast T1 and cartilage thickness as confounding factors in dGEMRIC when evaluating human cartilage adaptation to physical activity

Background: The dGEMRIC (delayed Gadolinium-Enhanced MRI of Cartilage) technique has been used in numerous studies for quantitative in vivo evaluation of the relative glycosaminoglycan (GAG) content in cartilage. The purpose of this study was to determine the influence of pre-contrast T1 and cartilage thickness when assessing knee joint cartilage quality with dGEMRIC. Methods: Cartilage thickness and T1 relaxation time were measured in the central part of the femoral condyles before and two hours after intravenous Gd-DTPA2- administration in 17 healthy volunteers from a previous study divided into two groups: 9 sedentary volunteers and 8 exercising elite runners. Results were analyzed in superficial and a deep weight-bearing, as well as in non-weight-bearing regions of interest. Results: In the medial compartment, the cartilage was thicker in the exercising group, in weight-bearing and non-weight-bearing segments. In most of the segments, the T1 pre-contrast value was longer in the exercising group compared to the sedentary group. Both groups had a longer pre-contrast T1 in the superficial cartilage than in the deep cartilage. In the superficial cartilage, the gadolinium concentration was independent of cartilage thickness. In contrast, there was a linear correlation between the gadolinium concentration and cartilage thickness in the deep cartilage region. Conclusion: Cartilage pre-contrast T1 and thickness are sources of error in dGEMRIC that should be considered when analysing bulk values. Our results indicate that differences in cartilage structure due to exercise and weight-bearing may be less pronounced than previously demonstrated