A strong sign language recognition system can break down the barriers that separate hearing and speaking members of society from speechless members. A novel fast recognition system with low computational cost for digital American Sign Language (ASL) is introduced in this research. Different image processing techniques are used to optimize and extract the shape of the hand fingers in each sign. The feature extraction stage includes a determination of the optimal threshold based on statistical bases and then recognizing the gap area in the zero sign and calculating the heights of each finger in the other digits. The classification stage depends on the gap area in the zero signs and the number of opened fingers in the other signs as well as the sequence in which the opened fingers appear for those that have the same number of opened fingers. The conducted test results showed the system’s high capability to classify all the digits; where both the precision and F-score percentages of the proposed model reached the desired optimal value (100%).
Neural cryptography deals with the problem of “key exchange” between two neural networks by using the mutual learning concept. The two networks exchange their outputs (in bits) and the key between two communicating parties ar eventually represented in the final learned weights, when the two networks are said to be synchronized. Security of neural synchronization is put at risk if an attacker is capable of synchronizing with any of the two parties during the training process.
Diabetes is one of the increasing chronic diseases, affecting millions of people around the earth. Diabetes diagnosis, its prediction, proper cure, and management are compulsory. Machine learning-based prediction techniques for diabetes data analysis can help in the early detection and prediction of the disease and its consequences such as hypo/hyperglycemia. In this paper, we explored the diabetes dataset collected from the medical records of one thousand Iraqi patients. We applied three classifiers, the multilayer perceptron, the KNN and the Random Forest. We involved two experiments: the first experiment used all 12 features of the dataset. The Random Forest outperforms others with 98.8% accuracy. The second experiment used only five att
... Show MoreShadow detection and removal is an important task when dealing with color outdoor images. Shadows are generated by a local and relative absence of light. Shadows are, first of all, a local decrease in the amount of light that reaches a surface. Secondly, they are a local change in the amount of light rejected by a surface toward the observer. Most shadow detection and segmentation methods are based on image analysis. However, some factors will affect the detection result due to the complexity of the circumstances. In this paper a method of segmentation test present to detect shadows from an image and a function concept is used to remove the shadow from an image.
As we live in the era of the fourth technological revolution, it has become necessary to use artificial intelligence to generate electric power through sustainable solar energy, especially in Iraq and what it has gone through in terms of crises and what it suffers from a severe shortage of electric power because of the wars and calamities it went through. During that period of time, its impact is still evident in all aspects of daily life experienced by Iraqis because of the remnants of wars, siege, terrorism, wrong policies ruling before and later, regional interventions and their consequences, such as the destruction of electric power stations and the population increase, which must be followed by an increase in electric power stations,
... Show MoreThe penalized least square method is a popular method to deal with high dimensional data ,where the number of explanatory variables is large than the sample size . The properties of penalized least square method are given high prediction accuracy and making estimation and variables selection
At once. The penalized least square method gives a sparse model ,that meaning a model with small variables so that can be interpreted easily .The penalized least square is not robust ,that means very sensitive to the presence of outlying observation , to deal with this problem, we can used a robust loss function to get the robust penalized least square method ,and get robust penalized estimator and
... Show MoreCompressing the speech reduces the data storage requirements, leading to reducing the time of transmitting the digitized speech over long-haul links like internet. To obtain best performance in speech compression, wavelet transforms require filters that combine a number of desirable properties, such as orthogonality and symmetry.The MCT bases functions are derived from GHM bases function using 2D linear convolution .The fast computation algorithm methods introduced here added desirable features to the current transform. We further assess the performance of the MCT in speech compression application. This paper discusses the effect of using DWT and MCT (one and two dimension) on speech compression. DWT and MCT performances in terms of comp
... Show MoreMembrane manufacturing system was operated using dry/wet phase inversion process. A sample of hollow fiber membrane was prepared using (17% wt PVC) polyvinyl chloride as membrane material and N, N Dimethylacetamide (DMAC) as solvent in the first run and the second run was made using (DMAC/Acetone) of ratio 3.4 w/w. Scanning electron microscope (SEM) was used to predict the structure and dimensions of hollow fiber membranes prepared. The ultrafiltration experiments were performed using soluble polymeric solute poly ethylene glycol (PEG) of molecular weight (20000 Dalton) 800 ppm solution 25 °C temperature and 1 bar pressure. The experimental results show that pure water permeation increased from 25.7 to 32.2 (L/m2.h.bar) by adding a
... Show MoreGypseous soils represented one of the most complex salty soils that faced the geotechnical engineers. Structures that built on gypsum soil will undergo unexpected distortions that will eventually contribute to catastrophic failure. The purpose of this article is to understand the durability of gypsum soil against wetting drying cycles after improvement with polyurethane polymer especially investigate the effect of the wetting-drying cycle on collapsibility. The soil was brought from Sawa lake in AL-Muthanna Governorate in Iraq, with gypsum content 65.5%, A set of Odometer tests were performed to determine the collapsibility potential (CP) for treated and untreated gypsum soil. The result shows that adding a different per
... Show MoreDifferent injection material types were tried in the injection of soft clay, such as lime (L), silica fume (SF), and leycobond-h (LH). In this study, experiments were made to study the effect of injection on soft clay consolidation settlement. A sample of natural soft clayey soil was investigated in the laboratory and the sample was injected with each of the grout materials used, L, SF, L + SF, and L + SF + LH. A 20 cm3 of each slurry grout was conducted into the soil, which was compacted in California Bearing Ratio (CBR) mold and cured for 7 days, and then the sample was loaded to 80 N load by a circular steel footing 60 mm in diameter. The settlement was r
In this paper we present the first ever measured experimental electron momentum density of Cu2Sb at an intermediate resolution (0.6 a.u.) using 59.54 keV 241Am Compton spectrometer. The measurements are compared with the theoretical Compton profiles using density function theory (DFT) within a linear combination of an atomic orbitals (LCAO) method. In DFT calculation, Perdew-Burke-Ernzerhof (PBE) scheme is employed to treat correlation whereas exchange is included by following the Becke scheme. It is seen that various approximations within LCAO-DFT show relatively better agreement with the experimental Compton data. Ionic model calculations for a number of configurations (Cu+x/2)2(Sb-x) (0.0≤x≤2.0) are also performed utilizing free a
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