Retained soft tissue foreign bodies following injuries are frequently seen in the Emergency and Plastic Surgery practice. The patients with such presentations require a watchful and detailed clinical as- sessment to overcome the anticipant possibility of missing them. However, the diagnosis based on the clinical evaluation is usually challenging and needs to be supported by imaging modalities that are suboptimal and may fail in identifying some types of foreign bodies. Owing to that, serious complications such as chronic pain, infection, and delayed wound healing can be faced that necessitate a prompt intervention to halt those detrimental consequences. The classical method of removal is a surgical exploration which is not free of risks.

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.

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Churning of employees from organizations is a serious problem. Turnover or churn of employees within an organization needs to be solved since it has negative impact on the organization. Manual detection of employee churn is quite difficult, so machine learning (ML) algorithms have been frequently used for employee churn detection as well as employee categorization according to turnover. Using Machine learning, only one study looks into the categorization of employees up to date.  A novel multi-criterion decision-making approach (MCDM) coupled with DE-PARETO principle has been proposed to categorize employees. This is referred to as SNEC scheme. An AHP-TOPSIS DE-PARETO PRINCIPLE model (AHPTOPDE) has been designed that uses 2-stage MCDM s

In present work an investigation for precise hole drilling via continuous wave (CW) CO2 laser at 150 W maximum output power and wavelength 10.6 μm was achieved with the assistance of computerized numerical controlled (CNC) machine and assist gases. The drilling process was done for thin sheets (0.1 – 0.3 mm) of two types of metals; stainless steel (sst) 321H, steel 33 (st). Changing light and process parameters such as laser power, exposure time and gas pressure was important for getting the optimum results. The obtained results were supported with computational results using the COMSOL 3.5a software code.

The microstructures of rapidly solidified laser clad layers of laser cladding of Inconel 617 with different nickel-aluminum premixed clad powders are discussed. The effect of different cladding speeds on the microstructures of rapidly solidified laser clad layers is discussed too. The detailed microstructural results showed that different growth mechanisms are produced during rapid solidification. These are planar, cellular, cellular/dendritic and dendritic.

Research on geopolitical and geopolitical studies relates to a range of sciences that can be called auxiliary sciences, such as political science, international relations in particular, and history, but their focus is rarely on science and ethnography

That the issues and problems of the world today has become so large and complex that does not allow a specific field or knowledge to solve one of the need for the newly known knowledge integration (geopolitics and geopolitics) in particular to move towards cognitive integration to understand many of the problems and global issues that faced The importance of this study comes to clarify the relationship between Darwinism, geopolitics and geopolitics. The geopolitics in modern terms d

Abstract :

The Aims of this research is to describe the concept of risk, its type and method of measurement, and to clarify the impact of these risks on the expected cash flow statement and the preparation of the target cash flow statement that takes these risks into consideration. Because the local economic environment is exposed to many risks, Therefore, this list will be predictive, which will help the economic unit to make administrative decisions, especially decisions related to operational, investment and financing activities. Therefore, the research problem is based on the fact that most of the local economic units are the list of flows According to the actual basis and not according to the discretionary basis (bud