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

Nowadays, people's expression on the Internet is no longer limited to text, especially with the rise of the short video boom, leading to the emergence of a large number of modal data such as text, pictures, audio, and video. Compared to single mode data ,the multi-modal data always contains massive information. The mining process of multi-modal information can help computers to better understand human emotional characteristics. However, because the multi-modal data show obvious dynamic time series features, it is necessary to solve the dynamic correlation problem within a single mode and between different modes in the same application scene during the fusion process. To solve this problem, in this paper, a feature extraction framework of

  Necessary and sufficient conditions for the operator equation I AXAX n  * , to have a real positive definite solution X are given. Based on these conditions, some properties of the operator A as well as relation between the solutions X andAare given.

The orientation allows the individual to develop the capacity to become aware of his personal characteristics and to develop them in order to choose his studies, his training and his professional activities, in all the conjunctures of his existence, with the joint concern for the collective future of solidarity and the fulfillment of one's personality and responsibility». (Danvers, 1992,p. 190). This advice states that: orientation is a process closely associated with education and formation. It becomes effective if the steps include broad information on education and trades, and focuses on the social development and moral construction of the individual. The objective of this survey is to show the influence of the orientation of the p

Mature oil reservoirs surrounded with strong edge and bottom water drive aquifers experience pressure depletion and water coning/cresting. This laboratory research investigated the effects of bottom water drive and gas breakthrough on immiscible CO2-Assisted Gravity Drainage (CO2-AGD), focusing on substantial bottom water drive. The CO2-AGD method vertically separates the injected CO2 to formulate a gas cap and Oil. Visual experimental evaluation of CO2-AGD process performance was performed using a Hele-Shaw model. Water-wet sand was used for the experiments. The gas used for injection was pure CO2, and the “oleic” phase was n-decane with a negative spreading coefficient. The aqueous phase was deionized water. To evaluate the feasibilit

<p>Analyzing X-rays and computed tomography-scan (CT scan) images using a convolutional neural network (CNN) method is a very interesting subject, especially after coronavirus disease 2019 (COVID-19) pandemic. In this paper, a study is made on 423 patients’ CT scan images from Al-Kadhimiya (Madenat Al Emammain Al Kadhmain) hospital in Baghdad, Iraq, to diagnose if they have COVID or not using CNN. The total data being tested has 15000 CT-scan images chosen in a specific way to give a correct diagnosis. The activation function used in this research is the wavelet function, which differs from CNN activation functions. The convolutional wavelet neural network (CWNN) model proposed in this paper is compared with regular convol

Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D

Abstract:

Background: The alteration of bowel habits, bleeding per-rectum and anemia were common features in both groups in this study, but in young patients there was a delay of 6 months between the presenting symptoms and the definitive diagnosis because the disease was not suspected and investigated in them. The most common site for the tumors in young patients was the rectum and in patients above the age of 40 years was the Sigmoid.

The pathological finding showed that classification of the colorectal tumors in young patients appear moderately to poorly differentiated adenocarcinoma , this indicate a more malignant course of the disease in young patients.

This study sen

Kirchhoff Time Migration method was applied in pre-and post-Stack Time Migration for post-processing of images collected from Balad-Samarra (BS-92) survey line that is sited across Ajeel anticline oilfield. The results showed that Ajeel anticline structure was relocated at the correct position in the migrated stacked section. The two methods (Pre and Post) of migration processing showed enhanced subsurface images and increased horizontal resolution, which was clear after the broadening the syncline and narrowing or compressing the anticline. However, each of these methods was associated with migration noise. Thus, a Post-Stack process was applied using Dip-Removal (DDMED) and Band-Pass filters to eliminate the artifact noise. The time-fr

Lattakia city faces many problems related to the mismanagement of solid waste, as the disposal process is limited to the random Al-Bassa landfill without treatment. Therefore, solid waste management poses a special challenge to decision-makers by choosing the appropriate tool that supports strategic decisions in choosing municipal solid waste treatment methods and evaluating their management systems. As the human is primarily responsible for the formation of waste, this study aims to measure the degree of environmental awareness in the Lattakia Governorate from the point of view of the research sample members and to discuss the effect of the studied variables (place of residence, educational level, gender, age, and professional status) o