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 learning model was utilized to resize images and feature extraction. Finally, different ML classifiers have been tested for recognition based on the extracted features. The effectiveness of each classifier was assessed using various performance metrics. The results show that the proposed system works well, and all the methods achieved good results; however, the best results obtained were for the Support Vector Machine (SVM) with a linear kernel.
Date palm fiber is one of the common wastes available in the M. E. countries essentially Iraq. The aim of search to investigate the performance and effects of fiber date palm on the mechanical properties of high strength concrete, this fiber was used in three ratio 2, 4 and 6 % by vol. of concrete at ages of (7, 28, 90) days. Results demonstrated improvement in the compressive strength increased 19.2 %, 23.6%, 24.9 % for 2%, 4%, 6% of fiber respectively at age 28 days. Flexural strength increases 47.6%, 66.2%, 93.8% form (2,4,6) % of fiber respectively at age 28 days. Density increase about 0.41%, 0, 61 % 0.69 % for (2,4,6) % of fiber respectively at age 28. Absorption water decrease
The purpose of this preliminary study is to verify the possibility of using Iraqi Zahdi date palm biomass as a resource for biogas production, methane in particular using thermophilic anaerobic digestion with waste water treatment activated sludge. Moreover, is to investigate the influence of extra nutrients addition to the digestion mixture. Biogas was captured in sealed jars with remote sensing modules connected to computer with integrated program to record the gas pressure continuously. A total gas pressure with 67% Methane was produced from date pulp waste fermentation with a yield of 0.57 Lit for each gram volatile solid of substrate. Addition of 1% yeast extract solution as nutrient increased Methane yield in liters by 5.9%. This i
... Show MorePalm vein recognition is a one of the most efficient biometric technologies, each individual can be identified through its veins unique characteristics, palm vein acquisition techniques is either contact based or contactless based, as the individual's hand contact or not the peg of the palm imaging device, the needs a contactless palm vein system in modern applications rise tow problems, the pose variations (rotation, scaling and translation transformations) since the imaging device cannot aligned correctly with the surface of the palm, and a delay of matching process especially for large systems, trying to solve these problems. This paper proposed a pose invariant identification system for contactless palm vein which include three main
... Show MoreOver the past few years, ear biometrics has attracted a lot of attention. It is a trusted biometric for the identification and recognition of humans due to its consistent shape and rich texture variation. The ear presents an attractive solution since it is visible, ear images are easily captured, and the ear structure remains relatively stable over time. In this paper, a comprehensive review of prior research was conducted to establish the efficacy of utilizing ear features for individual identification through the employment of both manually-crafted features and deep-learning approaches. The objective of this model is to present the accuracy rate of person identification systems based on either manually-crafted features such as D
... Show MoreThe area of character recognition has received a considerable attention by researchers all over the world during the last three decades. However, this research explores best sets of feature extraction techniques and studies the accuracy of well-known classifiers for Arabic numeral using the Statistical styles in two methods and making comparison study between them. First method Linear Discriminant function that is yield results with accuracy as high as 90% of original grouped cases correctly classified. In the second method, we proposed algorithm, The results show the efficiency of the proposed algorithms, where it is found to achieve recognition accuracy of 92.9% and 91.4%. This is providing efficiency more than the first method.