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.
The negative impact of oral diseases on the function, economy, and general health of the population is well‐documented. In the last decades, evidence linking increased expression of depression and oral diseases/conditions has significantly increased. The aim of this study is to assess the association between oral disease/conditions and self‐reported symptoms of depression individuals.
A specially designed questionnaire was distributed via social media for 1 week. It consisted of two main sections; the first section was dedicated to collect demographic variables and self‐reported symptoms
Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class
... Show MoreDate 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
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