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Fifth generation (5G) applications like Internet of Things (IoT), Enhanced Mobile Broadband (eMBB), Cognitive Radios (CR), Vehicle to Vehicle (V2V) and Machine to Machine (M2M) communication put new demands on the network in terms of low latency, ultra-reliable communication and efficiency when transmitting very small bursts. One new contender that makes its appearance recently is the Universal Filtered Multi- Carrier (UFMC). UFMC is a potential candidate to meet the requirements of 5G upcoming applications. This related waveform encounters the peak-to-average power ratio (PAPR) issue arising from the usage of multi-carrier transmission. In this investigation, two PAPR reduction techniques, called Per Subband Tone Reservation (PSTR) scheme to alleviate PAPR in UFMC systems are suggested. The first one is a pre-filtering PSTR scheme that uses the least squares approximation (LSA) algorithm to calculate the optimization factor(μ) and the second one is a post-filtering method. The concept of this proposal lies on the use of peaks reductions Tone to carry the correctional signal that reduces the high peaks of each sub-band individually. To shed light on UFMC as a potential waveform for 5G upcoming application, a comparison with OFDM modulation is done.

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]

End-to-End Encryption (E2EE) plays an essential role in safeguarding user privacy and protecting sensitive data across various communication platforms, including messaging applications, email services, and Internet of Things (IoT) devices. This paper presents a Hybrid Cryptography-Based E2EE method implemented on a Software Defined Networking (SDN) infrastructure, to strengthen bidirectional data security between hosts and IoT devices via the non-secure Message Queuing Telemetry Transport (MQTT) port. By addressing the threat of Man-in-the-Middle (MitM) attacks, the proposed system ensures that only authorized users can decrypt transmitted messages. This paper thoroughly analyzes the implementation and advantages of our Hybrid Cryptography-Based E2EE method by comparing RSA and ECC encryption techniques. ECC-256 is favored for key generation, owing to its high efficiency and speed, measured at 0.4009 ms. Additionally, through a comparison of RSA, AES, and ChaCha20 algorithms, AES-256 emerges as the optimal encryption choice, demonstrating the fastest encryption and decryption times for publishing 0.2758 ms and 0.1781 ms, respectively and for subscribing, with encryption at 0.2542 ms and decryption at 0.1577 ms. Along with its minimal packet size and low resource consumption, our proposed Hybrid Cryptography-Based E2EE method, implemented on SDN infrastructure, validate it's effectiveness in securing digital communications within SDN environments compared to existing solutions. [ABSTRACT FROM AUTHOR]

The integration of Artificial Intelligence (AI) into e-learning has transformed conventional educational approaches, improving the learning process and maximizing student achievement. This study offers a thorough examination of how AI can be utilized to enhance e-learning results by employing advanced predictive methods and performance optimization strategies. The main goals consist of creating an AI-based framework to monitor and analyze student interactions, evaluating the influence of online learning platforms on student understanding using advanced algorithms, and determining the most efficient methods for blended learning systems. AI algorithms, known for their cognitive ability and capacity to learn, adapt, and make decisions, are employed to analyze and forecast student performance, thereby improving educational quality and outcomes. The practical results obtained by implementing machine learning and deep learning models, such as convolutional neural networks (CNN) and recurrent neural networks (RNN), show substantial enhancements in forecasting different performance metrics. This research highlights the ability of AI to develop adaptable, effective, and successful e-learning environments, promoting enhanced academic achievement and customized learning experiences. The findings demonstrate that CNN outperformed other deep learning and machine learning algorithms in terms of accuracy during the prediction phase, showcasing the advanced capabilities of AI in educational contexts. Portions of this text were previously published as part of a preprint (https://doi.org/10.21203/rs.3.rs-4724603/v1). [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]

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]