Abstract 

For sparse system identification,recent suggested algorithms are  -norm Least Mean Square (  -LMS), Zero-Attracting LMS (ZA-LMS), Reweighted Zero-Attracting LMS (RZA-LMS), and p-norm LMS (p-LMS) algorithms, that have modified the cost function of the conventional LMS algorithm by adding a constraint of coefficients sparsity. And so, the proposed algorithms are named  -ZA-LMS, 

One of the diseases on a global scale that causes the main reasons of death is lung cancer. It is considered one of the most lethal diseases in life. Early detection and diagnosis are essential for lung cancer and will provide effective therapy and achieve better outcomes for patients; in recent years, algorithms of Deep Learning have demonstrated crucial promise for their use in medical imaging analysis, especially in lung cancer identification. This paper includes a comparison between a number of different Deep Learning techniques-based models using Computed Tomograph image datasets with traditional Convolution Neural Networks and SequeezeNet models using X-ray data for the automated diagnosis of lung cancer. Although the simple details p

Biometrics represent the most practical method for swiftly and reliably verifying and identifying individuals based on their unique biological traits. This study addresses the increasing demand for dependable biometric identification systems by introducing an efficient approach to automatically recognize ear patterns using Convolutional Neural Networks (CNNs). Despite the widespread adoption of facial recognition technologies, the distinct features and consistency inherent in ear patterns provide a compelling alternative for biometric applications. Employing CNNs in our research automates the identification process, enhancing accuracy and adaptability across various ear shapes and orientations. The ear, being visible and easily captured in

Background: Neonatal septicemia is a major health problem in developing countries furthermore data on bacteriological profile in early onst sepses (EOS) and late neonatal sepsis (LOS) are lacking in context of  continuous change in bacteriological profile and increasing resistant strains. Objectives: The study done to determine the pattern of organisms implicated in neonatal septicemia in a neonatal care unit and to measure the degree of bacterial resistance to some antibiotics.

Type of the study : cross –sectional study.

Methods: Confirmed cases of neonatal septicemia admitted at Al-Alwyia pediatric teaching hospital for the period from January 20

Aim: This study aims to determine both the level  of knowledge, attitude, and practice  in clinical breast examination in women and the relationship between knowledge and practice.Data collection was performed within a three-month period starting from February 2019 on.  Subjects and Method: A cross-sectional analytical study included 657 non-randomly selected women visiting primary health care centers in Baghdad city with an average of age between (20-59) years old.  Results: Only 51.8% ever heard of clinical breast examination. The mean knowledge score for clinical breast examination was (51.2±44). Only 5.5% of women performed regular clinical breast examinations. Participants who adequately practice clinical breast examinatio

Background: Personality could be defined broadly as those characteristics of individuals that describe the pattern of feeling, cognition and behaving. Personality types can be measured by using one of personality type’s measures. Personality has been proposed to influence the oral health. The purpose of the present study was to assess the influence of Personality Types on caries experience in relation to salivary testosterone hormone among teachers in Baghdad city. Materials and Methods: Female teachers (n=534) of randomly selected schools were subjected to personality questionnaire using The Riso-Hudson Enneagram Type Indicator. Teachers were examined to estimate the caries experience according to the decayed-missing and filled i