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

Software Defined Networking (SDN) with centralized control provides a global view and achieves efficient network resources management. However, using centralized controllers has several limitations related to scalability and performance, especially with the exponential growth of 5G communication. This paper proposes a novel traffic scheduling algorithm to avoid congestion in the control plane. The Packet-In messages received from different 5G devices are classified into two classes: critical and non-critical 5G communication by adopting Dual-Spike Neural Networks (DSNN) classifier and implementing it on a Virtualized Network Function (VNF). Dual spikes identify each class to increase the reliability of the classification

In this paper, wireless network is planned; the network is predicated on the IEEE 802.16e standardization by WIMAX. The targets of this paper are coverage maximizing, service and low operational fees. WIMAX is planning through three approaches. In approach one; the WIMAX network coverage is major for extension of cell coverage, the best sites (with Band Width (BW) of 5MHz, 20MHZ per sector and four sectors per each cell). In approach two, Interference analysis in CNIR mode. In approach three of the planning, Quality of Services (QoS) is tested and evaluated. ATDI ICS software (Interference Cancellation System) using to perform styling. it shows results in planning area covered 90.49% of the Baghdad City and used 1000 mob

<p>Currently, breast cancer is one of the most common cancers and a main reason of women death worldwide particularly in<strong> </strong>developing countries such as Iraq. our work aims to predict the type of tumor whether benign or malignant through models that were built using logistic regression and neural networks and we hope it will help doctors in detecting the type of breast tumor. Four models were set using binary logistic regression and two different types of artificial neural networks namely multilayer perceptron MLP and radial basis function RBF. Evaluation of validated and trained models was done using several performance metrics like accuracy, sensitivity, specificity, and AUC (area under receiver ope

Summary:This article discusses the topic of phraseological units with the names of wild animals in the Russian and Arabic languages in the aspect of their comparative semantic and cultural analysis, since a comparative analysis of the meanings of phraseological units of the Arabic and Russian languages, detection of coincidences and differences in the compared languages, is an important method for studying linguoculturology, since phraseological units represent a reflection of culture in the language

In aired and semiarid areas like Iraq, saline soil may be considered one of the major concerns. In addition to environmental effects, they may produce significant geotechnical hazards that could interrupt the structure stability depending on the salt type and its concentration. So, it is crucial to identify the degree of the soil salinity with a proper tool for getting a qualified assessment and consequently offering a suitable treatment. In this paper, the electrical resistivity technique has been employed to detect the degree of soil salinity by considering a new electronic system. The system used a single-phase Direct Current (DC) to Alternating Current (AC) inverter accompanied by a transformer. Natural soils became artificially saline

Genome sequencing has significantly improved the understanding of HIV and AIDS through accurate data on viral transmission, evolution and anti-therapeutic processes. Deep learning algorithms, like the Fined-Tuned Gradient Descent Fused Multi-Kernal Convolutional Neural Network (FGD-MCNN), can predict strain behaviour and evaluate complex patterns. Using genotypic-phenotypic data obtained from the Stanford University HIV Drug Resistance Database, the FGD-MCNN created three files covering various antiretroviral medications for HIV predictions and drug resistance. These files include PIs, NRTIs and NNRTIs. FGD-MCNNs classify genetic sequences as vulnerable or resistant to antiretroviral drugs by analyzing chromosomal information and id

With the fast-growing of neural machine translation (NMT), there is still a lack of insight into the performance of these models on semantically and culturally rich texts, especially between linguistically distant languages like Arabic and English. In this paper, we investigate the performance of two state-of-the-art AI translation systems (ChatGPT, DeepSeek) when translating Arabic texts to English in three different genres: journalistic, literary, and technical. The study utilizes a mixed-method evaluation methodology based on a balanced corpus of 60 Arabic source texts from the three genres. Objective measures, including BLEU and TER, and subjective evaluations from human translators were employed to determine the semantic, contextual an