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

This study included isolation of some active materials from Curcuma longa such as tannins, saponins and volatile oils with percentage of 59%, 31%, and 9% respectively. Also the study included the determination of minerals in Curcuma longa such as " Na, Ca and K" using Flame photometer. The concentrations of these minerals were (14 ppm),(10 ppm) and )76 ppm) respectively. The anti-bacterial activity study was performed for the active materials isolated from Curcuma longa against two genus of pathogenic bacteria, Escherichia Coli and Staphylococcus aurous by using agar-well diffusion method. It appeared from this study that all of the extraction have inhibitory effect on bacteria was used. The inhibition zone diameter varies with

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

The main aim of this study was to molecular identification and determine the antagonistic impact of rhizosphere Trichoderma spp. against some phytopathogenic fungi, including (Magnaporthe grisea) pyricularia oryzae, Rhizoctonia solani and Macrophomina phasolina. Four Trichoderma isolates were isolated from rhizosphere soils of the different host plants in different locations of Egyptian governorates. The morphological characterization of isolated Trichoderma as well as using of (ITS1-5.8S-ITS2) ribosomal gene sequence acquisition and data analyses. By comparing the results of DNA sequences of ITS region, the fungi represented one isolate were positively identified as T. asperellum (1 isolate T1) and one as T. longibrachiatum (1 isolate T2)