The Hopfield network is one of the easiest types, and its architecture is such that each neuron in the network connects to the other, thus called a fully connected neural network. In addition, this type is considered auto-associative memory, because the network returns the pattern immediately upon recognition, this network has many limitations, including memory capacity, discrepancy, orthogonally between patterns, weight symmetry, and local minimum. This paper proposes a new strategy for designing Hopfield based on XOR operation; A new strategy is proposed to solve these limitations by suggesting a new algorithm in the Hopfield network design, this strategy will increase the performance of Hopfield by modifying the architecture of the network, the training and the convergence phases, the proposed strategy based on size of pattern but will avoid learning similar pattern many time, whereas the new strategy XOR shows tolerance in the presence of noise-distorted patterns, infinite storage capacity and pattern inverse value. Experiments showed that the suggested method produced promising results by avoiding the majority of the Hopfield network's limitations. In additional it learns to recognize an infinite number of patterns with varying sizes while preserving a suitable noise ratio.
Background: Mini implant stability is primarily related to local bone density; no studies have evaluated bone density related to mini implant placement for orthodontic anchorage between different age groups in the maxilla and the mandible. The present research aims to evaluate side, gender, age, and regional differences in bone density of the alveolar bone at various orthodontic implant sites. Materials and method: Fifty three individuals who were divided into two groups according to their age into: group I (ages 16-20 years) and group II (ages 21-29 years) had subjected to clinical examination, then 64-multislice computed tomography scan data were evaluated and bone density was measured in Hounsfield unit at 102 points (51 in the maxilla
... Show MoreThis paper examines the impact of flexural strengthening on the percentage of damaged strands in internally unbonded tendons in partially prestressed concrete beams (0, 14.28%, and 28.57%) and the recovering conditions using CFRP composite longitudinal laminates at the soffit, and end anchorage U-wrap sheets to restore the original flexural capacity and mitigate the delamination of the soffit of longitudinal Carbon Fiber Reinforced Polymer (CFRP) laminates. The composition of the laminates and anchors affected the stress of the CFRP, the failure mode, and thus the behavior of the beam. The experimental results revealed that the usage of CFRP laminates has a considerable impact on strand strain, particularly when anchors are employed
... Show MoreSurvival analysis is widely applied in data describing for the life time of item until the occurrence of an event of interest such as death or another event of understudy . The purpose of this paper is to use the dynamic approach in the deep learning neural network method, where in this method a dynamic neural network that suits the nature of discrete survival data and time varying effect. This neural network is based on the Levenberg-Marquardt (L-M) algorithm in training, and the method is called Proposed Dynamic Artificial Neural Network (PDANN). Then a comparison was made with another method that depends entirely on the Bayes methodology is called Maximum A Posterior (MAP) method. This method was carried out using numerical algorithms re
... Show MoreComposite materials are widely used in the engineered assets as aerospace structures, marine and air navigation owing to their high strength/weight ratios. Detection and identification of damage in the composite structures are considered as an important part of monitoring and repairing of structural systems during the service to avoid instantaneous failure. Effective cost and reliability are essential during the process of detecting. The Lamb wave method is an effective and sensitive technique to tiny damage and can be applied for structural health monitoring using low energy sensors; it can provide good information about the condition of the structure during its operation by analyzing the propagation of the wave in the
... Show MoreAdvancements and modernizations introduced into the educational and pedagogical systems have significantly impacted teaching processes and how subjects are presented and explained to students. The focus has shifted to how learners interact with the material they need to learn, providing sufficient opportunities for learning and granting them freedom and self-confidence to achieve learning objectives. The research problem stems from the researcher's experience as a lecturer in the College of Physical Education and Sports Science, particularly in teaching basketball. She observed that some instructors were deficient in using the most effective teaching methods. The researcher formulated her research question based on these observations: "What
... Show MoreThe problem of generated waste as a result of the implementation of construction projects, has been aggravated recently because of construction activity experienced by the world, especially Iraq, which is going through a period of reconstruction, where construction waste represents (20-40%) of the total generated waste and has a negative effect on the environment and economic side of the project. In addition, the rate of consumpted amounts of natural resources are estimated to be about 40% in the construction industry, so it became necessary to reduce waste and to be manage well. This study aims to identify the key factors affecting waste management through the various phases of the project, and this is accom
... Show MoreThe study aimed to investigate the effect of different times as follows 0.5, 1.00, 2.00 and 3.00 hrs, type of solvent (acetone, methanol and ethanol) and temperature (~ 25 and 50)ºc on curcumin percentage yield from turmeric rhizomes. The results showed significant differences (p? 0.05) in all variables. The curcumin content which were determined spectrophotometrically ranged between (0.55-2.90) %. The maximum yield was obtained when temperature, time and solvent were 50ºC, 3 hrs and acetone, respectively.
The use of real-time machine learning to optimize passport control procedures at airports can greatly improve both the efficiency and security of the processes. To automate and optimize these procedures, AI algorithms such as character recognition, facial recognition, predictive algorithms and automatic data processing can be implemented. The proposed method is to use the R-CNN object detection model to detect passport objects in real-time images collected by passport control cameras. This paper describes the step-by-step process of the proposed approach, which includes pre-processing, training and testing the R-CNN model, integrating it into the passport control system, and evaluating its accuracy and speed for efficient passenger flow
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