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jkmc-326
Deep Vein Thrombosis Predisposing Factors Analysis Using Association Rules Mining
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Background: DVT is a very common problem with a very serious complications like pulmonary embolism (PE) which carries a high mortality,and many other chronic and annoying complications ( like chronic DVT, post-phlebitic syndrome, and chronic venous insufficiency) ,and it has many risk factors that affect its course, severity ,and response to treatment. Objectives: Most of those risk factors are modifiable, and a better understanding of the relationships between them can be beneficial for better assessment for liable pfatients , prevention of disease, and the effectiveness of our treatment modalities. Male to female ratio was nearly equal , so we didn’t discuss the gender among other risk factors. Type of the study:A cross- sectional study.Methods: Data taken from 114 patients with DVT were analyzed by association rules mining.Immobility was the most important risk factor. Results: Smoking add more risk to immobile, post operative patient. Age per se has no effect.100% of patients with long bone fracture, were immobile. Fever occurred in one third of post operative patients who develop DVT. Conclusions: Association rules mining allow better and faster analysis of more data with an interactive powerful system, which saves time and effort, and discovers the relations among many factors to one or more than one factors. So, we use this method for analysis in this study, and we get the above mentioned relations, which are important for the future management of DVT.

 
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Publication Date
Thu Oct 01 2020
Journal Name
Defence Technology
A novel facial emotion recognition scheme based on graph mining
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Recent years have seen an explosion in graph data from a variety of scientific, social and technological fields. From these fields, emotion recognition is an interesting research area because it finds many applications in real life such as in effective social robotics to increase the interactivity of the robot with human, driver safety during driving, pain monitoring during surgery etc. A novel facial emotion recognition based on graph mining has been proposed in this paper to make a paradigm shift in the way of representing the face region, where the face region is represented as a graph of nodes and edges and the gSpan frequent sub-graphs mining algorithm is used to find the frequent sub-structures in the graph database of each emotion. T

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Publication Date
Tue Dec 22 2020
Journal Name
Clinical And Experimental Dental Research
Prevalence and factors influencing reporting of true periodontal chief complaints: A retrospective analysis
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Abstract<sec><title>Objectives

To investigate the prevalence of true periodontal chief complaints (CC) and the factors affecting their reporting by patients with periodontal diseases (PD).

Materials and Methods

This cross‐sectional study was based on retrospective analysis of available periodontal records. Different personal and demographic variables were obtained from these records including CC, age, gender, working status, past medical/dental history, smoking status and diagnosis. In addition, clinical parameters of plaque index, gingival index, probing pocket depth (PPD), and number of missing teeth. Periodontal CC were r

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Publication Date
Fri Oct 14 2022
Journal Name
المجلة العراقية لعلوم التربة
REVIEW: USING MACHINE VISION AND DEEP LEARINING IN AUTOMATED SORTING OF LOCAL LEMONS
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Sorting and grading agricultural crops using manual sorting is a cumbersome and arduous process, in addition to the high costs and increased labor, as well as the low quality of sorting and grading compared to automatic sorting. the importance of deep learning, which includes the artificial neural network in prediction, also shows the importance of automated sorting in terms of efficiency, quality, and accuracy of sorting and grading. artificial neural network in predicting values and choosing what is good and suitable for agricultural crops, especially local lemons.

Publication Date
Tue Aug 10 2021
Journal Name
Design Engineering
Lossy Image Compression Using Hybrid Deep Learning Autoencoder Based On kmean Clusteri
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Image compression plays an important role in reducing the size and storage of data while increasing the speed of its transmission through the Internet significantly. Image compression is an important research topic for several decades and recently, with the great successes achieved by deep learning in many areas of image processing, especially image compression, and its use is increasing Gradually in the field of image compression. The deep learning neural network has also achieved great success in the field of processing and compressing various images of different sizes. In this paper, we present a structure for image compression based on the use of a Convolutional AutoEncoder (CAE) for deep learning, inspired by the diversity of human eye

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Publication Date
Fri Jul 01 2022
Journal Name
International Journal Of Nonlinear Analysis And Applications
Survey on distributed denial of service attack detection using deep learning: A review
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Distributed Denial of Service (DDoS) attacks on Web-based services have grown in both number and sophistication with the rise of advanced wireless technology and modern computing paradigms. Detecting these attacks in the sea of communication packets is very important. There were a lot of DDoS attacks that were directed at the network and transport layers at first. During the past few years, attackers have changed their strategies to try to get into the application layer. The application layer attacks could be more harmful and stealthier because the attack traffic and the normal traffic flows cannot be told apart. Distributed attacks are hard to fight because they can affect real computing resources as well as network bandwidth. DDoS attacks

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Publication Date
Sat Jun 06 2020
Journal Name
Journal Of The College Of Education For Women
Image classification with Deep Convolutional Neural Network Using Tensorflow and Transfer of Learning
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The deep learning algorithm has recently achieved a lot of success, especially in the field of computer vision. This research aims to describe the classification method applied to the dataset of multiple types of images (Synthetic Aperture Radar (SAR) images and non-SAR images). In such a classification, transfer learning was used followed by fine-tuning methods. Besides, pre-trained architectures were used on the known image database ImageNet. The model VGG16 was indeed used as a feature extractor and a new classifier was trained based on extracted features.The input data mainly focused on the dataset consist of five classes including the SAR images class (houses) and the non-SAR images classes (Cats, Dogs, Horses, and Humans). The Conv

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Publication Date
Sun Jun 06 2021
Journal Name
Materials
Strengthening of Continuous Reinforced Concrete Deep Beams with Large Openings Using CFRP Strips
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To accommodate utilities in buildings, different sizes of openings are provided in the web of reinforced concrete deep beams, which cause reductions in the beam strength and stiffness. This paper aims to investigate experimentally and numerically the effectiveness of using carbon fiber reinforced polymer (CFRP) strips, as a strengthening technique, to externally strengthen reinforced concrete continuous deep beams (RCCDBs) with large openings. The experimental work included testing three RCCDBs under five-point bending. A reference specimen was prepared without openings to explore the reductions in strength and stiffness after providing large openings. Openings were created symmetrically at the center of spans of the other specimens

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Publication Date
Sun Jul 31 2022
Journal Name
Iraqi Journal Of Science
A Prediction of Skin Cancer using Mean-Shift Algorithm with Deep Forest Classifier
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      Skin cancer is the most serious health problems in the globe because of its high occurrence compared to other types of cancer. Melanoma and non-melanoma are the two most common kinds of skin cancer. One of the most difficult problems in medical image processing is the automatic detection of skin cancer. Skin melanoma is classified as either benign or malignant based on the results of this test. Impediment due to artifacts in dermoscopic images impacts the analytic activity and decreases the precision level. In this research work, an automatic technique including segmentation and classification is proposed. Initially, pre-processing technique called DullRazor tool is used for hair removal process and semi-supervised mean-shift

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Publication Date
Wed Aug 15 2018
Journal Name
Al-khwarizmi Engineering Journal
Experimental and Simulation investigations of Micro Flexible Deep Drawing Using Floating Ring Technique
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Micro metal forming has an application potential in different industrial fields. Flexible tool-assisted sheet metal forming at micro scale is among the forming techniques that have increasingly attracted wide attention of researchers. This forming process is a suitable technique for producing micro components because of its inexpensive process, high quality products and relatively high production rate. This study presents a novel micro deep drawing technique through using floating ring as an assistant die with flexible pad as a main die. The floating ring designed with specified geometry is located between the process workpiece and the rubber pad. The function of the floating ring in this work is to produce SS304 micro cups with profile

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Publication Date
Mon Apr 11 2011
Journal Name
Icgst
Employing Neural Network and Naive Bayesian Classifier in Mining Data for Car Evaluation
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In data mining, classification is a form of data analysis that can be used to extract models describing important data classes. Two of the well known algorithms used in data mining classification are Backpropagation Neural Network (BNN) and Naïve Bayesian (NB). This paper investigates the performance of these two classification methods using the Car Evaluation dataset. Two models were built for both algorithms and the results were compared. Our experimental results indicated that the BNN classifier yield higher accuracy as compared to the NB classifier but it is less efficient because it is time-consuming and difficult to analyze due to its black-box implementation.