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

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

Consumer protection requires seeking one official entity to accomplish its tasks. The large number of devices control and implementation in it, return to several ministries, and executive bodies lose coordination and cooperation, and unite efforts to eliminate the types of fraud and control the quality of production within the approved standards. This is in addition to the seriousness of the existence of administrative and legal corruption which hinders the role of inspection committees and representatives of official bodies with the relationship and prevents them from carrying out their duties entrusted to them or presenting them with administrative complications to thwart their duties in consumer protection. This is despite the

Poly methyl methacrylate PMMA polymer could be considered the main material that used mostly in the recent years in denture base fabrication. It commonly known by it is poor strength properties such as low impact strength. The aim of the present research was to enhance the performance of PMMA denture base through the addition of two kind of nanoparticles (nano particles that selected from artificial and natural sources). Nano -particles from both Al2O3 and crushed peanut Peel were used for comparing purposes.Various weight fraction used in this study for both kinds of the additive (1%, 2% and 3%). Moreover, in this work a study and evaluation in impact strength (I.S.) value were done before and after immersion. The new prepared nanocompo

The impact of mental training overlap on the development of some closed and open skills in five-aside football for middle school students, Ayad Ali Hussein, Haidar Abedalameer Habe

   Lithology identification plays a crucial role in reservoir characteristics, as it directly influences petrophysical evaluations and informs decisions on permeable zone detection, hydrocarbon reserve estimation, and production optimization. This paper aims to identify lithology and minerals composition within the Mishrif Formation of the Ratawi Oilfield using well log data from five open hole logs of wells RT-2, RT-4, RT-5, RT-6, and RT-42. At this step, the logging lithology identification tasks often involve constructing a lithology identification model based on the assumption that the log data are interconnected. Lithology and minerals were identified using three empirical methods: Neutron-Density cross plots for lithology id

Electronic remote identification (ER-ID) is a new radio frequency (RF) technology that is initiated by the Federal Aviation Authorities (FAA). For security reasons, traffic control, and so on, ER-ID has been applied for drones by the FAA to enable them to transmit their unique identification and location so that unauthorized drones can be identified. The current limitation of the existing ER-ID algorithms is that the application is limited to the Wi-Fi and Bluetooth wireless controllers, which results in a maximum range of 10–20 m for Bluetooth and 50–100 m for Wi-Fi. In this study, a mathematical computing technique based on finite state automaton (FSA) is introduced to expand the range of the ER-ID RF system and reduce the ene