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

A field experiment was conducted during winter season of 2021 at a research station of college of agricultural engineering sciences, university of Baghdad to determine the response of active fertility percentage and seed yield and its components of faba bean (Vicia faba L. cv. Aguadulce) to distance between plants and spraying of nano and traditional boron. A Randomized Complete Block Design according to split-plots arrangement was used at three replicates. The main plots were three distances between plants (25, 35 and 45 cm), while the sub plots including spraying of distilled water only (control treatment), spraying of boron at a 100 mg L^-1 and spraying of nano boron at two concentrations (1

Zernike Moments has been popularly used in many shape-based image retrieval studies due to its powerful shape representation. However its strength and weaknesses have not been clearly highlighted in the previous studies. Thus, its powerful shape representation could not be fully utilized. In this paper, a method to fully capture the shape representation properties of Zernike Moments is implemented and tested on a single object for binary and grey level images. The proposed method works by determining the boundary of the shape object and then resizing the object shape to the boundary of the image. Three case studies were made. Case 1 is the Zernike Moments implementation on the original shape object image. In Case 2, the centroid of the s

With the rapid development of smart devices, people's lives have become easier, especially for visually disabled or special-needs people. The new achievements in the fields of machine learning and deep learning let people identify and recognise the surrounding environment. In this study, the efficiency and high performance of deep learning architecture are used to build an image classification system in both indoor and outdoor environments. The proposed methodology starts with collecting two datasets (indoor and outdoor) from different separate datasets. In the second step, the collected dataset is split into training, validation, and test sets. The pre-trained GoogleNet and MobileNet-V2 models are trained using the indoor and outdoor se

In this paper, an approach for object tracking that is inspired from human oculomotor system is proposed and verified experimentally. The developed approach divided into two phases, fast tracking or saccadic phase and smooth pursuit phase. In the first phase, the field of the view is segmented into four regions that are analogue to retinal periphery in the oculomotor system. When the object of interest is entering these regions, the developed vision system responds by changing the values of the pan and tilt angles to allow the object lies in the fovea area and then the second phase will activate. A fuzzy logic method is implemented in the saccadic phase as an intelligent decision maker to select the values of the pan and tilt angle based

Land Use / Land Cover (LULC) classification is considered one of the basic tasks that decision makers and map makers rely on to evaluate the infrastructure, using different types of satellite data, despite the large spectral difference or overlap in the spectra in the same land cover in addition to the problem of aberration and the degree of inclination of the images that may be negatively affect rating performance. The main objective of this study is to develop a working method for classifying the land cover using high-resolution satellite images using object based method. Maximum likelihood pixel based supervised as well as object approaches were examined on QuickBird satellite image in Karbala, Iraq. This study illustrated that

The integration of arti cial intelligence (AI), whether through devices or software, has become a critical tool in analyzing and evaluating technical performance. AI signi cantly contributes to enhancing athletic performance by enabling accurate data analysis and supporting educators in developing effective training programs and interactive curricula. This study addresses a noticeable gap in the literature regarding the attitudes and inclinations of educators toward AI in physical education and sport sciences—a gap often attributed to limited awareness and lack of access to moderntechnologies.Theprimaryaimofthestudyistoexaminethetendenciesandperceptionsoffemaleinstructorsin physical education and sport sciences toward the use of AI

Alpha shape theory for 3D visualization and volumetric measurement of brain tumor progression using magnetic resonance images