Audio-visual detection and recognition system is thought to become the most promising methods for many applications includes surveillance, speech recognition, eavesdropping devices, intelligence operations, etc. In the recent field of human recognition, the majority of the research be- coming performed presently is focused on the reidentification of various body images taken by several cameras or its focuses on recognized audio-only. However, in some cases these traditional methods can- not be useful when used alone such as in indoor surveillance systems, that are installed close to the ceiling and capture images right from above in a downwards direction and in some cases people don't look straight the cameras or it cannot be added in some area such as W.C. or sleeping room. Thus, its commonly difficult to identify any movement or breakthrough process, on the other hand when need to pursue suspect when enter a building or party to identify his location and/or listen to his speech only and isolate it from other voices or noises, the other. Hence, the use of the hybrid combination technique is very effective. In this work, we proposed a multimodal human recognition approach that utilizes both the face and audio and is based upon a deep convolutional neural network (CNN). Mainly, to solve the challenge of not capturing part of the body, final results of recognizing via separate CNNs of VGG Face16 and ResNet50 are joined together depending on the score-level combination by Weighted Sum rule to enhance recognition performance. The results show that the proposed system success to recognise each person from his voice and/or his face captured. In addition, the system can separate the person voice and isolate it from noisy environment and determine the existence of desired person.
Prenatal markers are commonly used in practice to screen for some foetal abnormalities. They can be biochemical or ultrasonic markers in addition to the newly used cell free Deoxyribonucleic Acid (DNA) estimation. This review aimed to illustrate the applications of the prenatal screening, and the reliability of these tests in detecting the presence of abnormal chromosomes such as trisomy-21, trisomy-18, and trisomy-13 in addition to neural tube defects. Prenatal markers can also be used in the anticipation of some obstetrical complications depending on levels of these markers in the mother’s circulation. In the developed countries, prenatal screening tests are regularly used during antenatal care period. Neural tube defects, numer
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Productivity estimating of ready mixed concrete batch plant is an essential tool for the successful completion of the construction process. It is defined as the output of the system per unit of time. Usually, the actual productivity values of construction equipment in the site are not consistent with the nominal ones. Therefore, it is necessary to make a comprehensive evaluation of the nominal productivity of equipment concerning the effected factors and then re-evaluate them according to the actual values.
In this paper, the forecasting system was employed is an Artificial Intelligence technique (AI). It is represented by Artificial Neural Network (ANN) to establish the predicted model to estimate wet ready mixe
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This paper presents a proposed neural network algorithm to solve the shortest path problem (SPP) for communication routing. The solution extends the traditional recurrent Hopfield architecture introducing the optimal routing for any request by choosing single and multi link path node-to-node traffic to minimize the loss. This suggested neural network algorithm implemented by using 20-nodes network example. The result shows that a clear convergence can be achieved by 95% valid convergence (about 361 optimal routes from 380-pairs). Additionally computation performance is also mentioned at the expense of slightly worse results.
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
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Abstract
This study investigated the optimization of wear behavior of AISI 4340 steel based on the Taguchi method under various testing conditions. In this paper, a neural network and the Taguchi design method have been implemented for minimizing the wear rate in 4340 steel. A back-propagation neural network (BPNN) was developed to predict the wear rate. In the development of a predictive model, wear parameters like sliding speed, applying load and sliding distance were considered as the input model variables of the AISI 4340 steel. An analysis of variance (ANOVA) was used to determine the significant parameter affecting the wear rate. Finally, the Taguchi approach was applied to determine
... Show MoreThe research aims to identify the impact of using the electronic participatory learning strategy according to internet programs in learning some basic basketball skills for middle first graders according to the curricular course, and the sample of research was selected in the deliberate way of students The first stage of intermediate school.As for the problem of research, the researchers said that there is a weakness in the levels of school students in terms of teaching basketball skills, which prompted the researchers to create appropriate solutions by using a participatory learning strategy.The researchers imposed statistically significant differences between pre and post-test tests, in favor of the post tests individually and in favor of
... Show MoreCurrently, one of the topical areas of application of machine learning methods is the prediction of material characteristics. The aim of this work is to develop machine learning models for determining the rheological properties of polymers from experimental stress relaxation curves. The paper presents an overview of the main directions of metaheuristic approaches (local search, evolutionary algorithms) to solving combinatorial optimization problems. Metaheuristic algorithms for solving some important combinatorial optimization problems are described, with special emphasis on the construction of decision trees. A comparative analysis of algorithms for solving the regression problem in CatBoost Regressor has been carried out. The object of
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The aim of the research is to identify the cognitive method (rigidity flexibility) of third-stage students in the collage of Physical Education and Sports Sciences at The University of Baghdad, as well as to recognize the impact of using the McCarthy model in learning some of skills in gymnastics, as well as to identify the best groups in learning skills, the experimental curriculum was used to design equal groups with pre test and post test and the research community was identified by third-stage students in academic year (2020-2021), the subject was randomly selected two divisions after which the measure of cognitive method was distributed to the sample, so the subject (32) students were distributed in four groups, and which the pre te
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