3D medical volume segmentation using hybrid multiresolution statistical approaches

This article is available through the Brunel Open Access Publishing Fund. Copyright © 2010 S AlZu’bi and A Amira.3D volume segmentation is the process of partitioning voxels into 3D regions (subvolumes) that represent meaningful physical entities which are more meaningful and easier to analyze and usable in future applications. Multiresolution Analysis (MRA) enables the preservation of an image according to certain levels of resolution or blurring. Because of multiresolution quality, wavelets have been deployed in image compression, denoising, and classification. This paper focuses on the implementation of efficient medical volume segmentation techniques. Multiresolution analysis including 3D wavelet and ridgelet has been used for feature extraction which can be modeled using Hidden Markov Models (HMMs) to segment the volume slices. A comparison study has been carried out to evaluate 2D and 3D techniques which reveals that 3D methodologies can accurately detect the Region Of Interest (ROI). Automatic segmentation has been achieved using HMMs where the ROI is detected accurately but suffers a long computation time for its calculations

Optimum range of angle tracking radars: a theoretical computing

In this paper, we determine an optimal range for angle tracking radars (ATRs) based on evaluating the standard deviation of all kinds of errors in a tracking system. In the past, this optimal range has often been computed by the simulation of the total error components; however, we are going to introduce a closed form for this computation which allows us to obtain the optimal range directly. Thus, for this purpose, we firstly solve an optimization problem to achieve the closed form of the optimal range (Ropt.) and then, we compute it by doing a simple simulation. The results show that both theoretical and simulation-based computations are similar to each other

DL Multi-sensor information fusion service selective information scheme for improving the Internet of Things based user responses

Multi-sensor information fusion aids different services to meet the application requirements through independent and joint data assimilation. The role of multiple sensors in smart connected applications helps to improve their efficiency regardless of the users. However, the assimilation of different information is subject to resource and time constraints at the time of application response. This results in partial fulfillment of the application services, and hence, this article introduces a service selective information fusion processing (SSIFP) scheme. The proposed scheme identifies service-specific sensor information for satisfying the application service demands. The identification process is eased with deep recurrent learning in determining the level of sensor information fusion. This level identification reduces the unavailability of services (resource constraint) and delays in application services (time constraint). Through this identification, the applications\u27 precise demands are detected, and selective fusion is performed to mitigate the issues above. The proposed system\u27s performance is verified using the metrics delay, fusion rate, service loss, and backlogs

The Impact of Managers Efficiency on Quality of Strategic Decision-making under Crisis Management: An Empirical Study on Private Hospitals in Baghdad-Iraq

Managers have essential role in considering the foundation in organization, to avoid risks and crises, their efficiency and ability to minimize risks if it should occur, also they should make the right decision at crisis management, at a high qualities as a results of good experienced, education, skills, and best practice. The main objective of this study is to explore the impact of managers’ efficiency on quality of strategic decision-making directly and indirectly through crisis management in private hospitals in Baghdad/ Iraq, the study population was private hospitals in Baghdad/ Iraq, and a sample was chosen randomly which consists of (100) managers (administrative and physicians), and a questionnaire was designed consisting of (44) phrases to gather the primary data from the study sample. Data were analyzed using relevant statistical methods like regression analysis and path analysis. The study came to show a high level of importance for all study variables, and showed there is a significant positive direct impact of managers’ efficiency on quality of strategic decision making also there is indirect impact (through crisis management), beside there is a significant positive direct impact of managers efficiency on crisis management rather than a significant impact of crisis management on quality of strategic decision making, in private hospitals in Baghdad/ Iraq. Key words: Decision-making, Quality of Strategic Decision-making, Crisis Management, Efficiency.

Analyzing fibrous tissue pattern in fibrous dysplasia bone images using deep R-CNN networks for segmentation

Predictive health monitoring systems help to detect human health threats in the early stage. Evolving deep learning techniques in medical image analysis results in efficient feedback in quick time. Fibrous dysplasia (FD) is a genetic disorder, triggered by the mutation in Guanine Nucleotide binding protein with alpha stimulatory activities in the human bone genesis. It slowly occupies the bone marrow and converts the bone cell into fibrous tissues. It weakens the bone structure and leads to permanent disability. This paper proposes the study of FD bone image analyzing techniques with deep networks. Also, the linear regression model is annotated for predicting the bone abnormality levels with observed coefficients. Modern image processing begins with various image filters. It describes the edges, shades, texture values of the receptive field. Different types of segmentation and edge detection mechanisms are applied to locate the tumor, lesion, and fibrous tissues in the bone image. Extract the fibrous region in the bone image using the region-based convolutional neural network algorithm. The segmented results are compared with their accuracy metrics. The segmentation loss is reduced by each iteration. The overall loss is 0.24% and the accuracy is 99%, segmenting the masked region produces 98% of accuracy, and building the bounding boxes is 99% of accuracy

A comprehensive review on medical diagnosis using machine learning

The unavailability of sufficient information for proper diagnosis, incomplete or miscommunication between patient and the clinician, or among the healthcare professionals, delay or incorrect diagnosis, the fatigue of clinician, or even the high diagnostic complexity in limited time can lead to diagnostic errors. Diagnostic errors have adverse effects on the treatment of a patient. Unnecessary treatments increase the medical bills and deteriorate the health of a patient. Such diagnostic errors that harm the patient in various ways could be minimized using machine learning. Machine learning algorithms could be used to diagnose various diseases with high accuracy. The use of machine learning could assist the doctors in making decisions on time, and could also be used as a second opinion or supporting tool. This study aims to provide a comprehensive review of research articles published from the year 2015 to mid of the year 2020 that have used machine learning for diagnosis of various diseases. We present the various machine learning algorithms used over the years to diagnose various diseases. The results of this study show the distribution of machine learningmethods by medical disciplines. Based on our review, we present future research directions that could be used to conduct further research

EFL paraphrasing skills with QuillBot: Unveiling students' enthusiasm and insights

EFL students' attitudes are crucial for the development of writing abilities, which in the age of cutting-edge technology depend extensively on artificial intelligence -mediated tools, and paraphrasing draws no exception. Therefore, this study aims to identify English as a foreign language student’s enthusiasm and insights about utilizing QuillBot to improve their paraphrasing skills. To achieve the study objectives, the quasi-experimental design was employed. Thirty-one preparatory year students were recruited to answer a questionnaire and semi-structured interview having verified the validity and reliability of the instruments. The sample of the test demonstrated that students improved their performance in synonyms, sentence structure, and word choice. The respondents hold high enthusiasm and insights toward utilizing QuillBot to improve their paraphrasing skills. In addition, students had positive feelings about utilizing QuillBot to improve their paraphrasing skills. In light of the findings, the researchers recommended employing QuillBot in a writing class while learning paraphrasing skills