This study presents a comprehensive examination of the Al-Zn-Mg-Cu alloy, focusing on the effects of high strain rate dynamic compression and heat treatment on its performance. The research aimed to understand the underlying microstructural mechanisms contributing to the enhanced mechanical properties of the alloy, which is critical for applications in aerospace and automotive industries. High

Steady State Visually Evoked Potentials (SSVEPs) are intrinsic responses to specific visual stimulus frequencies. When the retina is activated by a frequency ranging from 3.5 to 75 Hz, the brain produces electrical activity at the same frequency as the visual signal, or its multiples. Identifying the preferred frequencies of neurocortical dynamic processes is a benefit of SSVEPs. However, the time

This paper presents a comprehensive methodology for gender detection using hand palm images, leveraging image processing techniques and PySpark for scalable and efficient processing. The approach encompasses a meticulous image preprocessing pipeline, incorporating essential stages like grayscale conversion, the application ofthe Difference of Gaussians (DoG) filter, and adaptive histogram

High-Entropy Alloys: Design, Manufacturing, and Emerging Applications presents cutting-edge advances in the field of these materials, covering their mechanics, methods of manufacturing, and applications, all while emphasizing the link between their structure/microstructure and functional properties. The book starts with a section on the fundamentals of high-entropy alloys (HEAs), with chapters

Despite the fact that numerous equiatomic and nonequiatomic high-entropy alloys (HEAs) have been observed to form a single solid solution (SS), it has also been established that several intermediate phases can emerge, some of which exhibit the structural characteristics of intermetallic compounds (ICs) such as Laves, B2, sigma, L12, and amorphous phases. This phenomenon has been extensively

The exploration of sentiment analysis in multilingual contexts, particularly through the integration of deep learning techniques and knowledge graphs, represents a significant advance in language processing research. This study specifically concentrates on the Arabic language, addressing the challenges presented by its morphological complexity. While the primary focus is Arabic, the research also

The increasing sophistication of cyber attacks necessitates effective intrusion detection systems. We propose a novel intrusion detection method integrating deep learning with big data management using Apache Spark. Leveraging the comprehensive CSE-CIC-IDS2018 dataset, we apply extensive data preprocessing, including handling missing and unreliable values, duplicates, and redundant columns. In

The current study used cutting-edge techniques to experimentally test the early diagnosis of diabetes via retinal scans. The goal was to enable effective disease prediction and management by facilitating quick and precise medical diagnostics. Three processes were involved in the development of a Diabetic Retinopathy (DR) diagnosis tool: feature extraction, feature reduction, and image

Heart localization holds significant importance in the process of the diagnosis and treatment of heart diseases. Additionally, it plays an important role in planning the cardiac scanning protocol. This research focuses on heart localization by employing the multi-label classification task with the utilization of RES-Net50. The primary objective is to predict the slices containing the heart and

Predicting customer’s behavior is one of the great challenges and obstacles for business nowadays. Companies take advantage of identifying these future behaviors to optimize business outcomes and create more powerful marketing strategies. This work presents a novel real-time framework that can predict the customer’s next interaction and the time of that interaction (when that interaction takes