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This paper introduces a novel methodology for evaluating communication performance in rotating electric machines using Received Signal Strength Indication (RSSI) measurements coupled with artificial intelligence. The proposed approach focuses on assessing the quality of wireless signals in the complex, dynamic environment inside these machines, where factors like reflections, metallic surfaces, and rotational movements can significantly impact communication. RSSI is used as a key parameter to monitor real-time signal behavior, enabling a detailed analysis of communication reliability. The methodology comprises several stages, including data collection, preprocessing, feature extraction, and model training. Various machine learning models are implemented and evaluated. Among these, the SVM model with a Radial Basis Function (RBF) kernel outperforms others, achieving an accuracy of 97%, with high precision and recall scores, confirming its robustness in classifying RSSI data and handling complex signal behavior. The confusion matrix further supports the SVM model's accuracy, showing minimal misclassification. [ABSTRACT FROM AUTHOR]

This study presents a novel approach to enhancing the design and performance of OFDMA (Orthogonal Frequency Division Multiple Access) networks, with a particular focus on WiMAX (Worldwide Interoperability for Microwave Access) for Best Effort (BE) services. The proposed method integrates a robust Markovian analytical model with four advanced scheduling algorithms: throughput fairness, resource fairness, opportunistic scheduling, and throttling. A sophisticated simulator was developed, incorporating an ON/OFF traffic generator, user-specific wireless channels, and a dynamic central scheduler to validate the model's accuracy and evaluate its robustness by dynamically allocating radio resources per frame. The validation study showed that the proposed model reduced simulation time by over 90%, completing analytical calculations in just 15 min, compared to nearly 2 days for simulations using conventional scheduling algorithms. Performance metrics such as the average number of active users and resource utilization closely matched those from the validation study, confirming the model's accuracy. In the robustness study, the model consistently performed well across diverse traffic distributions (exponential and Pareto) and channel conditions. The proposed architecture increased network throughput by up to 25% and reduced latency under dynamic conditions, demonstrating its scalability, adaptability, and efficiency as a crucial solution for next-generation wireless communication systems. [ABSTRACT FROM AUTHOR]

With the changing face of the learning paradigms in education industry due to the impact of global pandemic, various new revolutions are observed. This study reveals the intentions of students and academicians towards improvement in e-learning system. The technology acceptance model is used to identify the acceptability and improvements expected by them. Certain factors are identified in the model along with categorizing them under various hypothesis parameters. A survey was conducted for collecting opinions from various respondents. The survey was communicated in various universities across Middle East Region. More than 20 universities participated for answering the questions. [ABSTRACT FROM AUTHOR]

The diagnosis of brain tumors through magnetic resonance imaging (MRI) has become highly significant in the field of medical science. Relying solely on MR imaging for the detection and categorization of brain tumors demands significant time, effort, and expertise from medical professionals. This underscores the need for an autonomous model for brain tumor diagnosis. Our study involves the application of a deep convolutional neural network (DCNN) to diagnose brain tumors from MR images. The application of these algorithms offers several benefits, including rapid brain tumor prediction, reduced errors, and enhanced precision. The proposed model is built upon the state‐of‐the‐art CNN architecture VGG16, employing a data augmentation approach. The dataset utilized in this paper consists of 3000 brain MR images sourced from Kaggle, with 1500 images reported to contain tumors. Through training and testing, the pretrained CNN model achieves a precision and classification accuracy rate of 96%, and the loss is 1%. Moreover, it achieves an average precision, recall, and F1‐score of 98.7%, 97.44%, and 98.06%, respectively. These evaluation metric values demonstrate the effectiveness of the proposed solution. [ABSTRACT FROM AUTHOR]

The metallic components of an electric machine exert a notable impact on the electromagnetic transmission between an external recipient and an internal shaft sensor. The RTMS (rotor temperature monitoring system) is aimed at boosting power transfer in the machine, regardless of the considerable impact of its components. This research assesses the influence of the components of an electric machine on shaft sensor transmission and specifically addresses the packaging needs of the receiver antenna. The study includes a comparative analysis of two antennas. HFSS (high frequency structure simulator) simulation was utilized to identify the antenna with a superior propagation factor. The principal contribution of this paper is the assessment of how the electrical machine body affects the transmission and propagation of rotor sensor signals, establishing a connection between these effects and the packaging criteria for the receiver antenna. Moreover, the paper presents an antenna design that capitalizes on the electrical machine body to enhance the power transmission effectiveness. The final section of this paper encompasses the experimental results obtained from the implementation of the RN171 antenna on the electrical machine, provides valuable insights into the propagation characteristics, and contributes to a comprehensive understanding of the electromagnetic dynamics within the system. [ABSTRACT FROM AUTHOR]