In this paper, integrated quantum neural network (QNN), which is a class of feedforward

neural networks (FFNN’s), is performed through emerging quantum computing (QC) with artificial neural network(ANN) classifier. It is used in data classification technique, and here iris flower data is used as a classification signals. For this purpose independent component analysis (ICA) is used as a feature extraction technique after normalization of these signals, the architecture of (QNN’s) has inherently built in fuzzy, hidden units of these networks (QNN’s) to develop quantized representations of sample information provided by the training data set in various graded levels of certainty. Experimental results presented here show that

The objective of this work is to design and implement a cryptography system that enables the sender to send message through any channel (even if this channel is insecure) and the receiver to decrypt the received message without allowing any intruder to break the system and extracting the secret information. In this work, we implement an interaction between the feedforward neural network and the stream cipher, so the secret message will be encrypted by unsupervised neural network method in addition to the first encryption process which is performed by the stream cipher method. The security of any cipher system depends on the security of the related keys (that are used by the encryption and the decryption processes) and their corresponding le

Through the researchers' acquaintance with the previous studies, the problem was identified as that the preparation of training curricula in all its units must be based on accurate scientific foundations. Positively affect the type of attack and its implication in the presence of correlational relations, whether direct or indirect, i.e., precedence in training and in preparing units Therefore, the researcher decided to build a causal model to know the relationships to show the best model of the direct straight attack. The study aimed to build a causal model for the most important physical measurements and kinetic capabilities of the direct straight attack in the research sample. The two researchers used the descriptive approach in t

Biscuits are a global snack due to their convenience, variety, and durability. Biscuits with nutritious ingredients are in demand as customers become more health conscious. This change led to interest about utilizing agricultural by-products to enhance the nutritional value of widely consumed foods. Mango (Mangifera indica L.), a frequently cultivated tropical fruit, produces vital by-products during its processing, mainly comprising peels and kernels. The by-products, comprising around 35–60% of the mango fruit's weight, are high in bioactive compounds including dietary fiber, polyphenols, carotenoids, and essential fatty acids. Mango peels and kernels, even with their nutritional potential, frequently neglected, resulting in ris

Biscuits are a global snack due to their convenience, variety, and durability. Biscuits with nutritious ingredients are in demand as customers become more health conscious. This change led to interest about utilizing agricultural by-products to enhance the nutritional value of widely consumed foods. Mango (Mangifera indica L.), a frequently cultivated tropical fruit, produces vital by-products during its processing, mainly comprising peels and kernels. The by-products, comprising around 35–60% of the mango fruit's weight, are high in bioactive compounds including dietary fiber, polyphenols, carotenoids, and essential fatty acids. Mango peels and kernels, even with their nutritional potential, frequently neglected, resulting in ris

This search has introduced the techniques of multi-wavelet transform and neural network for recognition 3-D object from 2-D image using patches. The proposed techniques were tested on database of different patches features and the high energy subband of discrete multi-wavelet transform DMWT (gp) of the patches. The test set has two groups, group (1) which contains images, their (gp) patches and patches features of the same images as a part of that in the data set beside other images, (gp) patches and features, and group (2) which contains the (gp) patches and patches features the same as a part of that in the database but after modification such as rotation, scaling and translation. Recognition by back propagation (BP) neural network as com

This search has introduced the techniques of multi-wavelet transform and neural network for recognition 3-D object from 2-D image using patches. The proposed techniques were tested on database of different patches features and the high energy subband of discrete multi-wavelet transform DMWT (gp) of the patches. The test set has two groups, group (1) which contains images, their (gp) patches and patches features of the same images as a part of that in the data set beside other images, (gp) patches and features, and group (2) which contains the (gp) patches and patches features the same as a part of that in the database but after modification such as rotation, scaling and translation. Recognition by back propagation (BP) neural network as

Face Recognition Systems (FRS) are increasingly targeted by morphing attacks, where facial features of multiple individuals are blended into a synthetic image to deceive biometric verification. This paper proposes an enhanced Siamese Neural Network (SNN)-based system for robust morph detection. The methodology involves four stages. First, a dataset of real and morphed images is generated using StyleGAN, producing high-quality facial images. Second, facial regions are extracted using Faster Region-based Convolutional Neural Networks (R-CNN) to isolate relevant features and eliminate background noise. Third, a Local Binary Pattern-Convolutional Neural Network (LBP-CNN) is used to build a baseline FRS and assess its susceptibility to d

In this study, a low-cost biosorbent, dead mushroom biomass (DMB) granules, was used for investigating the optimum conditions of Pb(II), Cu(II), and Ni(II) biosorption from aqueous solutions. Various physicochemical parameters, such as initial metal ion concentration, equilibrium time, pH value, agitation speed, particles diameter, and adsorbent dosage, were studied. Five mathematical models describing the biosorption equilibrium and isotherm constants were tested to find the maximum uptake capacities: Langmuir, Freundlich, Redlich-Peterson, Sips, and Khan models. The best fit to the Pb(II) and Ni(II) biosorption results was obtained by Langmuir model with maximum uptake capacities of 44.67 and 29.17 mg/g for these two ions, respectively, w

The purpose of this research is to investigate the impact of corrosive environment (corrosive ferric chloride of 1, 2, 5, 6% wt. at room temperature), immersion period of (48, 72, 96, 120, 144 hours), and surface roughness on pitting corrosion characteristics and use the data to build an artificial neural network and test its ability to predict the depth and intensity of pitting corrosion in a variety of conditions. Pit density and depth were calculated using a pitting corrosion test on carbon steel (C-4130). Pitting corrosion experimental tests were used to develop artificial neural network (ANN) models for predicting pitting corrosion characteristics. It was found that artificial neural network models were shown to be