Given the importance of possessing the digital competence (DC) required by the technological age, whether for teachers or students and even communities and governments, educational institutions in most countries have sought to benefit from modern technologies brought about by the technological revolution in developing learning and teaching and using modern technologies in providing educational services to learners. Since university students will have the doors to work opened in all fields, the research aims to know their level of DC in artificial intelligence (AI) applications and systems utilizing machine learning (ML) techniques. The descriptive approach was used, as the research community consisted of students from the University

Artificial intelligence (AI) is entering many fields of life nowadays. One of these fields is biometric authentication. Palm print recognition is considered a fundamental aspect of biometric identification systems due to the inherent stability, reliability, and uniqueness of palm print features, coupled with their non-invasive nature. In this paper, we develop an approach to identify individuals from palm print image recognition using Orange software in which a hybrid of AI methods: Deep Learning (DL) and traditional Machine Learning (ML) methods are used to enhance the overall performance metrics. The system comprises of three stages: pre-processing, feature extraction, and feature classification or matching. The SqueezeNet deep le

A new two-way nesting technique is presented for a multiple nested-grid ocean modelling system. The new technique uses explicit center finite difference and leapfrog schemes to exchange information between the different subcomponents of the nested-grid system. The performance of the different nesting techniques is compared, using two independent nested-grid modelling systems. In this paper, a new nesting algorithm is described and some preliminary results are demonstrated. The validity of the nesting method is shown in some problems for the depth averaged of 2D linear shallow water equation.

Deep Learning Techniques For Skull Stripping of Brain MR Images

Hiding technique for dynamic encryption text using encoding table and symmetric encryption method (AES algorithm) is presented in this paper. The encoding table is generated dynamically from MSB of the cover image points that used as the first phase of encryption. The Harris corner point algorithm is applied on cover image to generate the corner points which are used to generate dynamic AES key to second phase of text encryption. The embedded process in the LSB for the image pixels except the Harris corner points for more robust. Experimental results have demonstrated that the proposed scheme have embedding quality, error-free text recovery, and high value in PSNR.

Artificial intelligence (AI) is entering many fields of life nowadays. One of these fields is biometric authentication. Palm print recognition is considered a fundamental aspect of biometric identification systems due to the inherent stability, reliability, and uniqueness of palm print features, coupled with their non-invasive nature. In this paper, we develop an approach to identify individuals from palm print image recognition using Orange software in which a hybrid of AI methods: Deep Learning (DL) and traditional Machine Learning (ML) methods are used to enhance the overall performance metrics. The system comprises of three stages: pre-processing, feature extraction, and feature classification or matching. The SqueezeNet deep le

The acidity of spent lubricant was treated using sodium hydroxide solution. The effect of three variables on the treatment have been studied . These are mixing time rangingfrom 5-35 minutes, NaOH to lubricant weight ratio ranging from  0.25-1.25 and weight percentage  of NaOH ranging from  2-6 % .

The experimental design of Box-Wilson method is adopted to find a useful relationship between the three controllable variables and the lowering in the acidity of the spent lubricant. Then the effective variables and interactions are identified using the statistical analysis(F-test) of  three variable fractional design. The mathematical model is well represented  by a second order polynomial.

By

Shaky Baghdad heavy crude oil 22 API is processed by distillation and solvent extraction. The purpose of distillation is to separate the light distillates    (light fractions) which represent 35% of heavy crude oil, and to obtain the reduced crude oil. The heavy residue (9 API) is  extracted with Iraqi light naphtha to get the deasphaltened oil (DAO), the extraction carried out with temperature range of 20-75 ^oC, solvent to oil ratio 5-15:1(ml:g) and a mixing time of 15 minutes. In general, results show that API of DAO increased twice the API of reduced crude oil while sulfur and metals content decreased 20% and 50% respectively. Deasphaltened oil produced from various operating conditions blended with the

Wireless sensor applications are susceptible to energy constraints. Most of the energy is consumed in communication between wireless nodes. Clustering and data aggregation are the two widely used strategies for reducing energy usage and increasing the lifetime of wireless sensor networks. In target tracking applications, large amount of redundant data is produced regularly. Hence, deployment of effective data aggregation schemes is vital to eliminate data redundancy. This work aims to conduct a comparative study of various research approaches that employ clustering techniques for efficiently aggregating data in target tracking applications as selection of an appropriate clustering algorithm may reflect positive results in the data aggregati