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 growth of the use mobile phones, people have become increasingly interested in using Short Message Services (SMS) as the most suitable communications service. The popularity of SMS has also given rise to SMS spam, which refers to any unwanted message sent to a mobile phone as a text. Spam may cause many problems, such as traffic bottlenecks or stealing important users' information. This paper,  presents a new model that extracts seven features from each message before applying a Multiple Linear Regression (MLR) to assign a weight to each of the extracted features. The message features are fed into the Extreme Learning Machine (ELM) to determine whether they are spam or ham. To evaluate the proposed model, the UCI bench

The objective of the present study is to verify the actual carious lesion depth by laser
fluorescence technique using 650 nm CW diode laser in comparison with the histopathological
investigation. Five permanent molar teeth were extracted from adult individuals for different reasons
(tooth impaction, periodontal diseases, and pulp infections); their ages were ranging from 20-25 years
old. Different carious teeth with varying clinical stages of caries progression were examined. An
experimental laser fluorescence set-up was built to perform the work regarding in vitro detection and
quantification of occlusal dental caries and the determination of its actual clinical carious lesion depth by
650 nm CW diode laser (excitat

      The study was conducted for the detection of Aflatoxin B1(AFB1) in the serum and urine of 42 early and middle childhood patients (26 male and  16 female ) with renal function disease, liver function disease, in additional to atrophy in the growth and other symptoms depending on the information within consent obtained from each patient, in addition to 8 children, apparently healthy, as  the control. The technique of HPLC was used for the detection of AFB1 from all samples. The results showed that out of 42 patient children, 19 (45.2%) gave positive detection of AFB1 in the serum among all age groups patients with a mean of 0.88 ng/ml and a range of (0.12-3.04) ng/ml. This was compared with the cont

In this paper, thirty six samples of canned vegetables were collected randomly from
different markets in Baghdad city from October 2013 till March 2014. The study
includes identifying the concentration of some heavy metals (lead, nickel, zinc and iron)
by flameless atomic absorption spectrophotometery. It was found that the higher
concentrations of heavy metals in canned vegetables, was lead 1.179 ppm in olive,
nickel 0.9078 ppm in olive, while zinc 10.143 ppm green peas and iron 90.601ppm in
white asparagus; but the lower concentrations represents with lead 0.0021 ppm in green
asparagus, nickel 0.0202 ppm in mushroom, while zinc 0.528 ppm in white asparagus
and iron 4.061 ppm in green peas. Canned food has been r

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

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

      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 minimum err

      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

Snowfall is one of the most important natural phenomena that can be seen in the
winter which is considered as a rare phenomenon in the Middle East. Snow is
covering parts of mountains in northern Iraq and neighboring countries in the first
months of the year. In this research, the snow cover can be detected and monitored
using pseudo color method, and the Normalized Difference Snow INDECES
(NDSI). Snow cover is difficult to detect in true color satellite images because the
white color of snow cover is the same color with the cloud cover. Modis sensors that
carried by Terra and Equa satellites images have been used in different bands and
different resolution and the case studied in 4/2/2017, 5/2/2017.