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

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

It is well known that the rate of penetration is a key function for drilling engineers since it is directly related to the final well cost, thus reducing the non-productive time is a target of interest for all oil companies by optimizing the drilling processes or drilling parameters. These drilling parameters include mechanical (RPM, WOB, flow rate, SPP, torque and hook load) and travel transit time. The big challenge prediction is the complex interconnection between the drilling parameters so artificial intelligence techniques have been conducted in this study to predict ROP using operational drilling parameters and formation characteristics. In the current study, three AI techniques have been used which are neural network, fuzzy i

loaded reinforced concrete circular short columns. An experimental investigation into the behavior
of 24 short reinforced concrete columns with and without steel fibers was carried out. The columns
had a circular section (200 mm diameter and 900 mm long). Test variables include concrete
strength, spacing of spiral reinforcement, and inclusion of steel fibers. The axial stress and axial
strains were obtained and used to evaluate the effects of the presence of steel fibers. It was found
that the addition of steel fibers slightly improves the load carrying capacity of the tested columns
whereas it significantly enhances the ductility of these specimens. Test results also indicated that for
the same confinement parameter

This study aims to investigate the effect of changing skins material on the strength of sandwich plates with circular hole when subjected to mechanical loads. Theoretical, numerical and experimental analyses are done for sandwich plates with hole and with two face sheet materials. Theoretical analysis is performed by using sandwich plate theory which depends on the first order shear deformation theory for plates subjected to tension and bending separately. Finite element method was used to analyse numerically all cases by ANSYS program.

The sandwich plates were investigated experimentally under bending and buckling load separately. The relationship between stresses and the ratio of hole diameter to plate width (d/b) are built, by

 In this work polymeric composites were done from unsaturated polyester as a matrix reinforced with glass fiber type (E-glass) with two different volume fraction 20% & 40%. Fatigue tests showed that the number of fatigue cycles to failure limit for samples reinforced with uniform (woven Roving 0-90°) E-glass fiber and random (continuous fibers) with volume fraction 40% more than that for the same samples with volume fraction 20%. Also the fatigue results showed that the uniform samples failed with fatigue cycles more than that of random.

This research studies the development and synthesis of blended nanocomposites filled with Titanium dioxide (TiO2). Blended nanocomposites based on unsaturated polyester resin (UPR) and epoxy resins were synthesized by reactive blending. The optimum quantity from nano partical of titanium dioxide was selected and different weight proportions 1%, 3%, 5%, and 7% ratios of new epoxy are blended with UPR resin. The dielectric breakdown strength and thermal conductivity properties of the blended nanocomposites were compared with those of the basis material (UPR and 3% TiO2).The results show good compatibility epoxy resins with the UPR resin on blending, dielectric breakdown strength values  are higher while thermal conductivity values of

Background: Reduction in bond strength when bonding was done immediately after intracoronal bleaching procedure has been recognized. The purpose of this study is to assess the effect of antioxidants (10% sodium ascorbate (SA), 0.1M thiourea and7% sodium bicarbonate (SB)) on reversing bonding strength of composite resin to bleached dentin. Materials and method: Sixty upper 1st premolar teeth, were selected, the crowns of the teeth were embedded in acrylic resin blocks exposing a flat dentin from the buccal surface, then divided into 6 groups (10 samples each). Bleaching for the experimental groups was performed using 35% hydrogen peroxide bleaching gel (pola–office).Group A (Negative