Estimating an individual's age from a photograph of their face is critical in many applications, including intelligence and defense, border security and human-machine interaction, as well as soft biometric recognition. There has been recent progress in this discipline that focuses on the idea of deep learning. These solutions need the creation and training of deep neural networks for the sole purpose of resolving this issue. In addition, pre-trained deep neural networks are utilized in the research process for the purpose of facial recognition and fine-tuning for accurate outcomes. The purpose of this study was to offer a method for estimating human ages from the frontal view of the face in a manner that is as accurate as possible and takes into account the majority of the challenges faced by existing methods of age estimate. Making use of the data set that serves as the foundation for the face estimation system in this region (IMDB-WIKI). By performing preparatory processing activities to setup and train the data in order to collect cases, and by using the CNN deep learning method, which yielded results with an accuracy of 0.960 percent, we were able to reach our objective.
One of the most significant challenges of medical care is the infection of postoperative wounds, and conventional visual examination often fails to detect it early. This research proposes the design of an innovative, passive wireless telemetry system for non-intrusive monitoring of the wound-healing process. The system integrates a biocompatible resonance circuit (LC) with a high-sensitivity piezoresistive sensor based on MXene (Ti3C2Tx). It operates within the standard industrial and medical (ISM) band at 13.56 MHz.The detection mechanism in the system is based on the principle of "impedance modulation" (Impedance Modulation), which arises from changes in the sensor's resistance under physiological tissue pressure. The system was
... Show MoreThis article presents the results of an experimental investigation of using carbon fiber–reinforced polymer sheets to enhance the behavior of reinforced concrete deep beams with large web openings in shear spans. A set of 18 specimens were fabricated and tested up to a failure to evaluate the structural performance in terms of cracking, deformation, and load-carrying capacity. All tested specimens were with 1500-mm length, 500-mm cross-sectional deep, and 150-mm wide. Parameters that studied were opening size, opening location, and the strengthening factor. Two deep beams were implemented as control specimens without opening and without strengthening. Eight deep beams were fabricated with openings but without strengthening, while
... Show MoreIt is often needed in demographic research to modern statistical tools are flexible and convenient to keep up with the type of data available in Iraq in terms of the passage of the country far from periods of war and economic sanctions and instability of the security for a period of time . So, This research aims to propose the use of style nonparametric splines as a substitute for some of the compounds of analysis within the model Lee-Carter your appreciation rate for fertility detailed variable response in Iraq than the period (1977 - 2011) , and then predict for the period (2012-2031). This goal was achieved using a style nonparametric decomposition of singular value vehicles using the main deltoid , and then estimate the effect of time-s
... Show MoreThe aim of this research is to diagnose the impact of competitive dimensions represented by quality, cost, time, flexibility on the efficiency of e-learning, The research adopted the descriptive analytical method by identifying the impact of these dimensions on the efficiency of e-learning, as well as the use of the statistical method for the purpose of eliciting results. The research concluded that there is an impact of the competitive dimensions on the efficiency of e-learning, as it has been proven that the special models for each of the research hypotheses are statistically significant and at a level of significance of 5%, and that each of these dimensions has a positive impact on the dependent variable, and the research recommended
... Show MoreMachine Learning (ML) algorithms are increasingly being utilized in the medical field to manage and diagnose diseases, leading to improved patient treatment and disease management. Several recent studies have found that Covid-19 patients have a higher incidence of blood clots, and understanding the pathological pathways that lead to blood clot formation (thrombogenesis) is critical. Current methods of reporting thrombogenesis-related fluid dynamic metrics for patient-specific anatomies are based on computational fluid dynamics (CFD) analysis, which can take weeks to months for a single patient. In this paper, we propose a ML-based method for rapid thrombogenesis prediction in the carotid artery of Covid-19 patients. Our proposed system aims
... Show MoreHM Al-Dabbas, RA Azeez, AE Ali, IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING, 2023
Hierarchical temporal memory (HTM) is a biomimetic sequence memory algorithm that holds promise for invariant representations of spatial and spatio-temporal inputs. This article presents a comprehensive neuromemristive crossbar architecture for the spatial pooler (SP) and the sparse distributed representation classifier, which are fundamental to the algorithm. There are several unique features in the proposed architecture that tightly link with the HTM algorithm. A memristor that is suitable for emulating the HTM synapses is identified and a new Z-window function is proposed. The architecture exploits the concept of synthetic synapses to enable potential synapses in the HTM. The crossbar for the SP avoids dark spots caused by unutil
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