Leishmania tropica is a species of flagellate parasites that infects humans and the cause of the disease cutaneous leishmaniasis, which is the most common form of leishmaniasis. It is one of the major parasites, which have high prevalence than other parasites in Iraq. The aim was to investigate the role of HLA alleles in susceptibility to cutaneous leishmaniasis infection in Baghdad in a sample of Iraqi patients. Cross sectional study (thirty Iraqi Arab Muslims patients with Leishmania tropica infection and thirty Iraqi Arab Muslims healthy persons) were participated in this study. Patients were consulted Department of Dermatology in Medical city Teaching hospital and AL Yarmook Teaching hospital for the period between March 2014 till May 2

The performance of photovoltaic (PV) panel having staggered metal foam fins was examined experimentally in Baghdad, Iraq. Three staggered metal foam fin configurations attached to the backside of the PV panel were studied. The measured parameters were front and back surfaces temperature, open voltage and current circuits, maximum power, and PV efficiency. It was noted that the maximum electrical efficiency enhancement was 4.7% for staggered metal foam fins (case III) than the reference PV panel. The operating temperature of the cell was increased when the value of solar intensity was high. Thereby, the electrical efficiency was decreased. It was found that the metal foam fins decreased the PV temperature by 2-3 ^o

This research focus on studying 3 types of Bakhour in the markets of Baghdad city and assessing their impact on the quality of life for asthmatic whom used Bakhour at their houses through investigating particles physical properties, also estimating the levels of heavy metals (Cd, Cu, Mn, Pb and Zn), Particulate Matter PM2.5, PM10, Total Volatile Organic Compounds (TVOC) and formaldehyde (HCHO). The quality of life for asthmatic patients whom use Bakhour was assessing by Mini Asthma Quality of Life Questionnaire. The results indicated that shapes of Bakhour particles were irregular or spherical. Burning process generated the higher percent of PM ˂1μm. Type 2 Bakhour showed the highest percent of <1μm which was 73%.The amount of

ECG is an important tool for the primary diagnosis of heart diseases, which shows the electrophysiology of the heart. In our method, a single maternal abdominal ECG signal is taken as an input signal and the maternal P-QRS-T complexes of original signal is averaged and repeated and taken as a reference signal. LMS and RLS adaptive filters algorithms are applied. The results showed that the fetal ECGs have been successfully detected. The accuracy of Daisy database was up to 84% of LMS and 88% of RLS while PhysioNet was up to 98% and 96% for LMS and RLS respectively.

The aim of the present study was to distinguish between healthy children and those with epilepsy by electroencephalography (EEG). Two biomarkers including Hurst exponents (H) and Tsallis entropy (TE) were used to investigate the background activity of EEG of 10 healthy children and 10 with epilepsy. EEG artifacts were removed using Savitzky-Golay (SG) filter. As it hypothesize, there was a significant changes in irregularity and complexity in epileptic EEG in comparison with healthy control subjects using t-test (p< 0.05). The increasing in complexity changes were observed in H and TE results of epileptic subjects make them suggested EEG biomarker associated with epilepsy and a reliable tool for detection and identification of this di

With the rapid development of computers and network technologies, the security of information in the internet becomes compromise and many threats may affect the integrity of such information. Many researches are focused theirs works on providing solution to this threat. Machine learning and data mining are widely used in anomaly-detection schemes to decide whether or not a malicious activity is taking place on a network. In this paper a hierarchical classification for anomaly based intrusion detection system is proposed. Two levels of features selection and classification are used. In the first level, the global feature vector for detection the basic attacks (DoS, U2R, R2L and Probe) is selected. In the second level, four local feature vect

The recent emergence of sophisticated Large Language Models (LLMs) such as GPT-4, Bard, and Bing has revolutionized the domain of scientific inquiry, particularly in the realm of large pre-trained vision-language models. This pivotal transformation is driving new frontiers in various fields, including image processing and digital media verification. In the heart of this evolution, our research focuses on the rapidly growing area of image authenticity verification, a field gaining immense relevance in the digital era. The study is specifically geared towards addressing the emerging challenge of distinguishing between authentic images and deep fakes – a task that has become critically important in a world increasingly reliant on digital med

      Data mining has the most important role in healthcare for discovering hidden relationships in big datasets, especially in breast cancer diagnostics, which is the most popular cause of death in the world. In this paper two algorithms are applied that are decision tree and K-Nearest Neighbour for diagnosing Breast Cancer Grad in order to reduce its risk on patients. In decision tree with feature selection, the Gini index gives an accuracy of %87.83, while with entropy, the feature selection gives an accuracy of %86.77. In both cases, Age appeared as the  most effective parameter, particularly when Age<49.5. Whereas  Ki67  appeared as a second effective parameter. Furthermore, K- Nearest Neighbor is based on the minimu