In this work, monitoring of monthly variation (from May 2016 to October 2016) in the concentration of the metals (Co, Zn, Cd, Pb, Ni and Fe) from Al-Diwaniya city of Iraq. Investigation about the pollution with these metals was achieved from five selected sites locate in study area by flame atomic absorption spectroscopy. The results showed a wide variation in the levels of heavy metals from site to site and from month to month. A total of 180 surface soil samples were analyzed to detecting the pollution with selected samples. The resultsshowed that the highest concentration with Ni was 6.290 mg kg-1 while the lowest concentration detected with Ni was 0.080 mg kg-1. The results of pollution index (enrichment factor, contamination factor, po

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Five subsurface sections covering the entire length of the Jeribe Limestone Formation (Early Middle Miocene) were studied from four oilfields in northern Iraq. It is hoped to unravel this formation microfacies ; depositional environment; diagenetic attributes and their parental processes; and the relationship between these processes and the observed porosity patterns. The microfacies were found to include mudstone, wackestone, packstone, and grainstone, which have been deposited respectively in open platform, restricted platform, and edge platform which represent the lagoonal environment, while the deposits of the lower parts of the Jeribe formation especially in well Hamrin- 2 reflect a deeper fore slope environment. By using the lithofac

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.

      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

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