This work bases on encouraging a generous and conceivable estimation for modified an algorithm for vehicle travel times on a highway from the eliminated traffic information using set aside camera image groupings. The strategy for the assessment of vehicle travel times relies upon the distinctive verification of traffic state. The particular vehicle velocities are gotten from acknowledged vehicle positions in two persistent images by working out the distance covered all through elapsed past time doing mollification between the removed traffic flow data and cultivating a plan to unequivocally predict vehicle travel times. Erbil road data base is used to recognize road locales around road segments which are projected into the commended camera

High-resolution imaging of celestial bodies, especially the sun, is essential for understanding dynamic phenomena and surface details. However, the Earth's atmospheric turbulence distorts the incoming light wavefront, which poses a challenge for accurate solar imaging. Solar granulation, the formation of granules and intergranular lanes on the sun's surface, is important for studying solar activity. This paper investigates the impact of atmospheric turbulence-induced wavefront distortions on solar granule imaging and evaluates, both visually and statistically, the effectiveness of Zonal Adaptive Optics (AO) systems in correcting these distortions. Utilizing cellular automata for granulation modelling and Zonal AO correction methods,

One of the diseases on a global scale that causes the main reasons of death is lung cancer. It is considered one of the most lethal diseases in life. Early detection and diagnosis are essential for lung cancer and will provide effective therapy and achieve better outcomes for patients; in recent years, algorithms of Deep Learning have demonstrated crucial promise for their use in medical imaging analysis, especially in lung cancer identification. This paper includes a comparison between a number of different Deep Learning techniques-based models using Computed Tomograph image datasets with traditional Convolution Neural Networks and SequeezeNet models using X-ray data for the automated diagnosis of lung cancer. Although the simple details p

problem of the research is the decline of the role of urban space with time as an influential system in societal relations. The research aims to define indicators for achieving social interaction in the city, and to determine indicators for achieving integration in the urban space, and to study the relationship between the integration of urban space and community interaction over time. the research assumed that by distinguishing the social interaction space from the urban space and developing urban spaces in order to be truly interactive spaces, this will help us achieve social interaction and build a positive relationship between them, which enables us to achieve integration within the urban spaces leading to social interaction. Because

Biometrics represent the most practical method for swiftly and reliably verifying and identifying individuals based on their unique biological traits. This study addresses the increasing demand for dependable biometric identification systems by introducing an efficient approach to automatically recognize ear patterns using Convolutional Neural Networks (CNNs). Despite the widespread adoption of facial recognition technologies, the distinct features and consistency inherent in ear patterns provide a compelling alternative for biometric applications. Employing CNNs in our research automates the identification process, enhancing accuracy and adaptability across various ear shapes and orientations. The ear, being visible and easily captured in

Semiconductor laser is used in processing many issues related to the scientific, military, medical, industrial and agricultural fields due to its unique properties such as coherence and high strength where GaN-based components are the most efficient in this field. Current technological developments mention to the strong connection of GaN with sustainable electronic and optoelectronic devices which have high-efficiency. The threshold current density of Al0.1Ga0.9N/GaN triple quantum well laser structure was investigated to determine best values of the parameters affecting the threshold current density that are well width, average thickness of active region, cavity length, reflectivity of cavity mirrors and optical confinement factor. The opt

Mycobacterium tuberculosis resistance to rifampicin is mainly mediated through mutations in the rpoB gene. The effects of rpoB mutations are relieved by secondary mutations in rpoA or rpoC genes. This study aims to identify mutations in rpoB, rpoA, and rpoC genes of Mycobacterium tuberculosis isolates and clarify their contribution to rifampicin resistance. Seventy isolates were identified by acid-fast bacilli smear, Genexpert assay, and growth on Lowenstein Jensen medium. Drug susceptibility, testing was performed by the proportional method.  DNA extraction, PCR, and sequencing were accomplished for the entire rpoA, rpoB, and

Gestational Diabetes Mellitus (GDM) is the most common metabolic disorder that found during gestation and is define as hyperglycemia of variable severity with onset or first recognition during gestation that does not clearly characterize any form of the preexisting diabetes (American Diabetes Association [1]). It affects approximately 16.5% of pregnancies worldwide (Plows, et al.[2]). The placenta is an organ that connects the mother and her fetus during pregnancy (Gul, et al.[3]). In the placenta, glucose can be transformed into glycogen for storage by either glycogen synthase or using glycogenin as a prime. However, the function of glycogen deposition stays a matter of debate, it may be the source of fuel for placenta itself or the storag