Locking of the knee is a one of the commonest orthopedic outpatient presentation. This patient usually need magnetic resonance imaging (MRI) when there is suspected lesion in the soft tissue clinically. Meniscal tears is the first differential diagnosis when accompany with painful knee. (1, 2)Giant cell tumor (GCT) is benign a localized nodular tenosynovitis often occur in the tendon sheath , Mostly involve the hand tendons in middle age group between 30 and 50 years old , female affect more than male.(3,4) The WHO defines two well-known kinds of giant cell tumor: (1) pigmented villonodular synovitis ( generalized type), which mainly involve the joints of the lower limb and (2) giant cell tumor of the tendon sheath ( localized type)

Locking of the knee is a one of the commonest orthopedic outpatient presentation. This patient usually need magnetic resonance imaging (MRI) when there is suspected lesion in the soft tissue clinically. Meniscal tears is the first differential diagnosis when accompany with painful knee. (1, 2)
Giant cell tumor (GCT) is benign a localized nodular tenosynovitis often occur in the tendon sheath , Mostly involve the hand tendons in middle age group between 30 and 50 years old , female affect more than male.(3,4) The WHO defines two well-known kinds of giant cell tumor: (1) pigmented villonodular synovitis ( generalized type), which mainly involve the joints of the lower limb and (2) giant cell tumor of the tendon sheath ( localized type)

This research aims to analyze and simulate biochemical real test data for uncovering the relationships among the tests, and how each of them impacts others. The data were acquired from Iraqi private biochemical laboratory. However, these data have many dimensions with a high rate of null values, and big patient numbers. Then, several experiments have been applied on these data beginning with unsupervised techniques such as hierarchical clustering, and k-means, but the results were not clear. Then the preprocessing step performed, to make the dataset analyzable by supervised techniques such as Linear Discriminant Analysis (LDA), Classification And Regression Tree (CART), Logistic Regression (LR), K-Nearest Neighbor (K-NN), Naïve Bays (NB

DeepFake is a concern for celebrities and everyone because it is simple to create. DeepFake images, especially high-quality ones, are difficult to detect using people, local descriptors, and current approaches. On the other hand, video manipulation detection is more accessible than an image, which many state-of-the-art systems offer. Moreover, the detection of video manipulation depends entirely on its detection through images. Many worked on DeepFake detection in images, but they had complex mathematical calculations in preprocessing steps, and many limitations, including that the face must be in front, the eyes have to be open, and the mouth should be open with the appearance of teeth, etc. Also, the accuracy of their counterfeit detectio

Many water supplies are now contaminated by anthropogenic sources such as domestic and agricultural waste, as well as manufacturing activities, the public's concern about the environmental effects of wastewater contamination has grown. Several traditional wastewater treatment methods, such as chemical coagulation, adsorption, and activated sludge, have been used to eliminate pollution; however, there are several drawbacks, most notably high operating costs, because of its low operating and repair costs, the usage of aerobic waste water treatment as a reductive medium is gaining popularity. Furthermore, it is simple to produce and has a high efficacy and potential to degrade pollu

Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an ob

Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an object under de

In the image processing’s field and computer vision it’s important to represent the image by its information. Image information comes from the image’s features that extracted from it using feature detection/extraction techniques and features description. Features in computer vision define informative data. For human eye its perfect to extract information from raw image, but computer cannot recognize image information. This is why various feature extraction techniques have been presented and progressed rapidly. This paper presents a general overview of the feature extraction categories for image.