Automated clinical decision support system (CDSS) acts as new paradigm in medical services today. CDSSs are utilized to increment specialists (doctors) in their perplexing decision-making. Along these lines, a reasonable decision support system is built up dependent on doctors' knowledge and data mining derivation framework so as to help with the interest the board in the medical care gracefully to control the Corona Virus Disease (COVID-19) virus pandemic and, generally, to determine the class of infection and to provide a suitable protocol treatment depending on the symptoms of patient. Firstly, it needs to determine the three early symptoms of COVID-19 pandemic criteria (fever, tiredness, dry cough and breathing difficulty) used to diagnose the person being infected by COVID-19 virus or not. Secondly, this approach divides the infected peoples into four classes, based on their immune system risk level (very high degree, high degree, mild degree, and normal), and using two indices of age and current health status like diabetes, heart disorders, or hypertension. Where, these people are graded and expected to comply with their class regulations. There are six important COVID-19 virus infections of different classes that should receive immediate health care to save their lives. When the test is positive, the patient age is considered to choose one of the six classifications depending on the patient symptoms to provide him the suitable care as one of the four types of suggested treatment protocol of COVID-19 virus infection in COVID-19 DSS application. Finally, a report of all information about any classification case of COVID-19 infection is printed where this report includes the status of patient (infection level) and the prevention protocol. Later, the program sends the report to the control centre (medical expert) containing the information. In this paper, it was suggested the use of C4.5 Algorithm for decision tree.

Nowadays, the reinforcement of concrete with natural fibers can consider being an effectual scheme to achieve the global demand for sustainable development. Due to sustainability, bio degradability, and environmental friendly, natural fibers are preferred as compared to synthetic fibers. The present study investigated the effect of width and thickness of jute fiber strips on the mechanical properties of reinforced concrete beams (RC beams). The experimental program consisted testing of twenty-four RC beams (150*150*1000 mm) comprised of four groups. The first group consisted of three reference RC beams, the second group consisted of three RC beams strengthened longitudinally with carbon fiber strip (CFRP) of 15 cm width, the third group included nine RC beams strengthened longitudinally with one layer of jute fiber strips (JFRP) having variable width, 5, 10, and 15 cm, and lastly the fourth group which was same as the third group except using double layer of jute fiber strips. Generally, the results showed that toughness, ultimate flexural strength, and load carrying capacity of RC beams strengthened with JFRP were increased with the increase of the strip width and thickness. On the other hand, ductility and stiffness were decreased with the increase of the strip width. Test results showed that load carrying capacity was improved by 5.56 and 11.1% for one layer of jute fiber strips of 5 and 15 cm width respectively as compared with the reference specimens. On the other hand, the load carrying capacity was improved by 3.95 and 8.75 % for two layers of jute fiber strips of 10 and 15 cm width respectively as compared with the one layer strengthened specimens. Concerning the CFRP strengthening, the load carrying capacity was improved by 77.76% as compared with the reference specimens.

The technology <span>of the multimodal brain image registration is the key method for accurate and rapid diagnosis and treatment of brain diseases. For achieving high-resolution image registration, a fast sub pixel registration algorithm is used based on single-step discrete wavelet transform (DWT) combined with phase convolution neural network (CNN) to classify the registration of brain tumors. In this work apply the genetic algorithm and CNN clasifcation in registration of magnetic resonance imaging (MRI) image. This approach follows eight steps, reading the source of MRI brain image and loading the reference image, enhencment all MRI images by bilateral filter, transforming DWT image by applying the DWT2, evaluating (fitness function) each MRI image by using entropy, applying the genetic algorithm, by selecting the two images based on rollout wheel and crossover of the two images, the CNN classify the result of subtraction to normal or abnormal, “in the eighth one,” the Arduino and global system for mobile (GSM) 8080 are applied to send the message to patient. The proposed model is tested on MRI Medical City Hospital in Baghdad database consist 550 normal and 350 abnormal and split to 80% training and 20 testing, the proposed model result achieves the 98.8% </span>accuracy.

Many of the researches have been successful in the field of computer-aided diagnosis because of the important results the intelligent computing approaches have achieved in this field. In this paper the robust classification method is presented, that attempts to classify the tissue suspicion region as normal or not normal by using a Fuzzy Inference System (FIS) using the Fuzzy C-Mean (FCM) clustering for fuzzification of the Gray-Level Co-Occurrence Matrix (GLCM) feature and a match shape function for fuzzification of matrix shape, then by using (T-norm) generate 729 rules (243 rules based on normal DB case, 243 rules based on benign case, 243 rules based on malignant case), after that the best Eighteen rules are selected (best 6 rules based on normal DB case, best 6 rules based on benign DB case, best 6 rules based on malignant DB case) by using genetic algorithm, then make summation for each group if the summation of 6 rules based on normal DB is greater than other summation of two group (best 6 rules based on benign DB case and best 6 rules based on malignant DB case) that mean resulted of the classification step is normal. The model approved efficiency classification rate of 97.5% of input dataset image.

This work presents an approach to deal with modelling a decision support system framework to introduce an application for decisions in medical knowledge system analysis. First aid is extremely important worldwide and, hence, a decision support framework, know as First Aid Decision Support System (FADSS), was designed and implemented to access experimental cases exerting danger to the general population, offering advanced conditions for testing abilities in research and arranging an emergency treatment through the graphical user interface (UI). The design of first aid treatment in FADSS depends on the general cases in first aid. We presented a strategy to manage first aid treatment  by modelling an application (FADSS) that assists people in finding information about first aid cases that are available as an application service. FADSS service uses a set of 15 important situations that might happen to people. The decision for treatment is suggested on the system for a different accident. FADSS tests the information in real-time by using mathematical models, decision tree and data mining (C4.5 algorithm) that are used in this research to build a decision making system. The system automatically sends warnings when the case is very critical, using text messages as email reports. The main objective of this study is to provide an efficient tool that helps people and junior workers in first aid centres in order to find the available resources of information.

A significant amount of data is gathered by the healthcare sector, but it is not appropriately mined and utilized. Finding these hidden links and patterns is frequently underutilized. Our study focuses on this element of medical diagnostics by identifying patterns in the information gathered about kidney illness, liver disease, and chronic pancreatitis (CP) and designing adaptive medical decision support systems (MDSS) to assist doctors. This research compares a variety of data mining (DM) techniques, knowledge extraction tools, and software platforms for usage in a DSS for analysis using the Waikato environment for knowledge analysis (WEKA) mining tool (decision tree (DT)). The objective is to determine the most significant risk factors based on the extraction of the categorization criteria. The datasets used for this work are illustrates how successfully DM and DSS are integrated. In this research, we suggest using the C4.5 DT algorithm, Naïve Bayes (NB) algorithm, and the logistic regression (LR) algorithm to categorize these diseases and evaluate their performance and accuracy rates. It inferred that the C4.5 algorithm accuracy is 0.873% which is better than the other two algorithms in terms of rule generation and accuracy.

No abstract

In this research, porcelain powder and glass was added by 0.25% (0.15% porcelain powder and 0.1% glass powder) to the epoxy used in the concrete strengthening. The addition improved epoxy properties and reduced the cost. The results showed that the addition of porcelain and glass powder decrease the interaction temperature by 11.37%, epoxy flow by 10.41%, and increase the compressive strength by 7.61%. Upon testing the improved epoxy cubes under the effect of temperatures up to 200ᵒ C compression resistance decreased by 6.36%, while both modulus of rupture and modulus of elasticity improved by (1.03% and 4.11%) respectively. The epoxy was tested and used to strengthen reinforced concrete beams and tests showed good improvement in flexural properties.