This abstract focuses on the significance of wireless body area networks (WBANs) as a cutting-edge and self-governing technology, which has garnered substantial attention from researchers. The central challenge faced by WBANs revolves around upholding quality of service (QoS) within rapidly evolving sectors like healthcare. The intricate task of managing diverse traffic types with limited resources further compounds this challenge. Particularly in medical WBANs, the prioritization of vital data is crucial to ensure prompt delivery of critical information. Given the stringent requirements of these systems, any data loss or delays are untenable, necessitating the implementation of intelligent algorithms. These algorithms play a pivotal role in expediting diagnosis and treatment processes during medical emergencies. This study introduces an innovative protocol termed collaborative binary Naive Bayes decision tree (CBNBDT) designed to enhance packet classification and transmission prioritization. Through the utilization of this protocol, incoming packets are categorized based on their respective classes, enabling subsequent prioritization. Thorough simulations have demonstrated the superior performance of the proposed CBNBDT protocol compared to baseline approaches.
The aim of the research is to identify the effect of instructional design according to Kagan structure among the first intermediate school student’s, and how skills could help in generating information in mathematics. In accordance with the research objectives, the researcher has followed the experimental research method by adopting an experimental design with two equivalent groups of post-test to measure skills in generating information. Accordingly, the researcher raised two main null hypotheses: there were no statistically significant differences at the level of significance (0.05) between the average scores of the experimental group who studied the material according to Kagan structure and th
... Show MoreIn this paper, an analytical solution describing the deflection of a cracked beam repaired with piezoelectric patch is introduced. The solution is derived using perturbation method. A novel analytical model to calculate the proper dimensions of piezoelectric patches used to repair cracked beams is also introduced. This model shows that the thickness of the piezoelectric patch depends mainly on the thickness of the cracked beam, the electro-mechanical properties of the patch material, the applied load and the crack location. Furthermore, the model shows that the length of the piezoelectric patches depends on the thickness of the patch as well as it depends on the length of the cracked beam and the crack depth. The additional flexibil
... Show MoreIn this paper, an analytical solution describing the deflection of a cracked beam repaired with piezoelectric patch is introduced. The solution is derived using perturbation method. A novel analytical model to calculate the proper dimensions of piezoelectric patches used to repair cracked beams is also introduced. This model shows that the thickness of the piezoelectric patch depends mainly on the thickness of the cracked beam, the electro-mechanical properties of the patch material, the applied load and the crack location. Furthermore, the model shows that the length of the piezoelectric patches depends on the thickness of the patch as well as it depends on the length of the cracked beam and the crack depth. The additio
... Show MoreIn the current worldwide health crisis produced by coronavirus disease (COVID-19), researchers and medical specialists began looking for new ways to tackle the epidemic. According to recent studies, Machine Learning (ML) has been effectively deployed in the health sector. Medical imaging sources (radiography and computed tomography) have aided in the development of artificial intelligence(AI) strategies to tackle the coronavirus outbreak. As a result, a classical machine learning approach for coronavirus detection from Computerized Tomography (CT) images was developed. In this study, the convolutional neural network (CNN) model for feature extraction and support vector machine (SVM) for the classification of axial
... Show MoreThe chemical properties of chemical compounds and their molecular structures are intimately connected. Topological indices are numerical values associated with chemical molecular graphs that help in understanding the physicochemical properties, chemical reactivity and biological activity of a chemical compound. This study obtains some topological properties of second and third dominating David derived (DDD) networks and computes several K Banhatti polynomial of second and third type of DDD.
Maintenance of machine tools can be improved significantly by analyzing the operating of manufacturing process with the real-time monitoring system for 3-D single point deformation measurements. Therefore, the process of manufacturing could be optimized with less cost. Recently, wireless technology and internet of things (IOT) applied on intelligent machine has witnessed a significant advance with augmented virtuality, the analysis and the process certainly would contribute to enhance the intelligence of that machine. This paper presents a group of the wireless sensors and 3D animation technologies for data monitoring and analyzing. Three degree of freedom robotic hand structure has been selected as a prototype to be form the process of the
... Show More This paper describes the application of consensus optimization for Wireless Sensor Network (WSN) system. Consensus algorithm is usually conducted within a certain number of iterations for a given graph topology. Nevertheless, the best Number of Iterations (NOI) to reach consensus is varied in accordance with any change in number of nodes or other parameters of . graph topology. As a result, a time consuming trial and error procedure will necessary be applied
to obtain best NOI. The implementation of an intellig ent optimization can effectively help to get the optimal NOI. The performance of the consensus algorithm has considerably been improved by the inclusion of Particle Swarm Optimization (PSO). As a case s
The complexity and variety of language included in policy and academic documents make the automatic classification of research papers based on the United Nations Sustainable Development Goals (SDGs) somewhat difficult. Using both pre-trained and contextual word embeddings to increase semantic understanding, this study presents a complete deep learning pipeline combining Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN) architectures which aims primarily to improve the comprehensibility and accuracy of SDG text classification, thereby enabling more effective policy monitoring and research evaluation. Successful document representation via Global Vector (GloVe), Bidirectional Encoder Representations from Tra
... Show MorePedagogical stylistics refers to the application of the tools of stylistics in the teaching of the English language as a foreign or a second language. Teaching and learning poetry is challenging. Thus, pedagogical corpus stylistics (Henceforth, PCS) approach has been introduced to Iraqi undergraduate foreign language learners (EFL) to guide them to analyze poetic language. The study aims to make students interact with authentic examples of poetic language and answer questions about it. The main objective of the study is to examine whether PCS tools enable the learners to provide linguistic evidence from the poetic texts they are exposed to. This in turn ensures objective poetic analyses. Moreover, it aims to enable EFL Iraqi stu
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