Problem: Cancer is regarded as one of the world's deadliest diseases. Machine learning and its new branch (deep learning) algorithms can facilitate the way of dealing with cancer, especially in the field of cancer prevention and detection. Traditional ways of analyzing cancer data have their limits, and cancer data is growing quickly. This makes it possible for deep learning to move forward with its powerful abilities to analyze and process cancer data. Aims: In the current study, a deep-learning medical support system for the prediction of lung cancer is presented. Methods: The study uses three different deep learning models (EfficientNetB3, ResNet50 and ResNet101) with the transfer learning concept. The three models are trained using a

The current study aims to investigate the effect of strategic knowledge management practices on an excellent performance at the Institution of Industrial Development and Research- the Ministry of Iraqi Industry (IDRMII). The present research is designed according to the descriptive method. To achieve the mentioned research objective, the researchers used the questionnaire as the main data collection tool. The research sample was 150 managers who are working at the top and middle management levels. To analyses the data gathered and reaching the results, several statistical techniques were used within AMOS.V25, SPSS.V21Software, This study reached a set of results, the most important of which is the existence of a positive correlat

Background: Periodontal pathogens can induce free radicals over-formation and thus may cause collagen and periodontal destruction. Anti-oxidants are used as supplements to counteract the over production of free radicals in periodontal disease, that can reduce of collagen destruction. Coenzyme Q10 serves as an endogenous antioxidant, regenerates other antioxidants, stimulates cell growth, and inhibits cell death. Because it is an antioxidant, coenzyme Q10 has received much research attention associated with periodontal diseases. Perio Q gel may possibly be effective as a topical agent and as an adjunct to scaling& root planing in treatment of gingivitis and chronic periodontitis. Aim of study:Determine the periodontal health status in a foll

In this paper,the homtopy perturbation method (HPM) was applied to obtain the approximate solutions of the fractional order integro-differential equations . The fractional order derivatives and fractional order integral are described in the Caputo and Riemann-Liouville sense respectively. We can easily obtain the solution from convergent the infinite series of HPM . A theorem for convergence and error estimates of the HPM for solving fractional order integro-differential equations was given. Moreover, numerical results show that our theoretical analysis are accurate and the HPM can be considered as a powerful method for solving fractional order integro-diffrential equations.

The influence of an aortic aneurysm on blood flow waveforms is well established, but how to exploit this link for diagnostic purposes still remains challenging. This work uses a combination of experimental and computational modelling to study how aneurysms of various size affect the waveforms. Experimental studies are carried out on fusiform-type aneurysm models, and a comparison of results with those from a one-dimensional fluid–structure interaction model shows close agreement. Further mathematical analysis of these results allows the definition of several indicators that characterize the impact of an aneurysm on waveforms. These indicators are then further studied in a computational model of a systemic blood flow network. This demonstr

Abstract

 The problem of missing data represents a major obstacle before researchers in the process of data analysis in different fields since , this problem is a recurrent one in all fields of study including social , medical , astronomical and clinical experiments .

The presence of such a problem within the data to be studied may influence negatively on the analysis and it may lead to misleading conclusions , together with the fact that these conclusions that result from a great bias caused by that problem in spite of the efficiency of wavelet methods but they are also affected by the missing of data , in addition to the impact of the problem of miss of accuracy estimation

A substantial portion of today’s multimedia data exists in the form of unstructured text. However, the unstructured nature of text poses a significant task in meeting users’ information requirements. Text classification (TC) has been extensively employed in text mining to facilitate multimedia data processing. However, accurately categorizing texts becomes challenging due to the increasing presence of non-informative features within the corpus. Several reviews on TC, encompassing various feature selection (FS) approaches to eliminate non-informative features, have been previously published. However, these reviews do not adequately cover the recently explored approaches to TC problem-solving utilizing FS, such as optimization techniques.