The rapid evolution of wireless communication has driven the need for sixth-generation (6G) networks, which aim to deliver unprecedented data rates, ultra-low latency, and seamless connectivity. Terahertz (THz) frequencies are a cornerstone of 6G technology due to their vast spectrum availability, but they introduce new challenges such as severe path loss, atmospheric attenuation, and security vulnerabilities. To overcome these issues, AI-driven beamforming has gained attention as a powerful solution for optimizing signal transmission and interference mitigation. However, existing AI-based methods remain susceptible to adversarial attacks, privacy breaches, and suboptimal adaptation in dynamic environments [1]. This paper introduces a federated learning (FL)-based AI-driven beamforming approach tailored for THz-enabled 6G networks. The framework ensures privacy-preserving intelligence by training beamforming models collaboratively across distributed edge devices, eliminating the need for centralized data sharing. To enhance security, we integrate adversarial defense techniques, strengthening resilience against potential attacks that could degrade beamforming accuracy. Through extensive simulations, we evaluate key performance metrics, including beamforming efficiency, spectral efficiency, signal-to-noise ratio (SNR), and resistance to adversarial perturbations. Our results indicate that the proposed FL-based beamforming approach improves adaptability, mitigates security threats, and enhances overall network performance compared to traditional centralized AI models. This study provides a scalable and secure AI-driven solution for 6G beamforming, paving the way for reliable and privacy-aware THz communications. Future work will explore real-world deployment and the integration of quantum-secure encryption techniques to further fortify security in 6G networks.
The evolution of 5G New Radio (NR) technology offers unprecedented speeds, ultra-low latency, and the capability to connect billions of devices. However, these advancements come with significant challenges, particularly in managing interference during uplink communication. This study presents a comprehensive investigation into the optimization of 5G NR uplink performance by focusing on two critical channels: the Physical Uplink Shared Channel (PUSCH) and the Physical Random Access Channel (PRACH). The research explores the impact of intra-cell and inter-cell interference on these channels, highlighting how various User Equipment (UE) and cell configuration parameters influence performance. Key Performance Indicators (KPIs) such as Block Error Rate (BLER) and Correct Detection Rate (CDR) are utilized to assess the effectiveness of proposed interference management strategies. Through rigorous simulations and empirical evaluations, the study provides valuable insights into optimizing 5G NR networks, aiming to enhance the robustness and reliability of uplink communication in diverse interference scenarios. The findings underscore the importance of adaptive resource allocation and interference mitigation techniques in achieving superior network performance and quality of service (QoS).
INTRODUCTION: An accurate prediction in soft tissue changes is of great importance for orthodontic treatment planning. Patients find it difficult to imagine how their facial appearance may change after orthodontic treatment without a visual reference. Predicting the postoperative facial appearance may thus be useful for managing expectations, easing communication, and researching different treatment choices. Computer-assisted programs are still relatively expensive and are not portable in comparison to smartphones, and the accuracy of soft tissue profile prediction of these android applications has not been thoroughly assessed. The purpose of the study is to assess how well the Webceph cephalometric Android application predicts changes in soft tissue profile following orthodontic treatment.MATERIALS AND METHOD: A total of 50 patients were screened for eligibility, and 24 young adult patients (8 males, 16 females; mean age 24.8 ±3.9 years) were finally included in the study based on the inclusion and exclusion criteria. The landmarks and parameters of the Legan and Burstone soft tissue analysis were used for the cephalometric analyses. The cephalometric tracings of the actual treatment result and the Webceph predicted treatment outcome was superimposed to calculate the prediction errors. Paired t-test used to compare the statistical differences between the predicted and actual treatment outcomes of the parameters used in the legan and burstone soft tissue analysis.RESULTS: There were significant differences between the predicted and actual values in parameters of legan and burstone soft tissue analysis (P\0.05). It was reported that the prediction in two parameters (i.e., Lower face throat (Sn-Gn-C angle) (Cm-sn-ls) Nasolabial angle) was a significant difference from the actual modifications in class I bimaxillary protrusion group and there were substantial changes in the prediction of two characteristics (facial convexity (G-Sn-Pg angle) and inter labial (Stms-Stmi) in the class II group.CONCLUSIONS: The Webceph VTO prediction in soft tissue changes after the orthodontic treatment in patients with bimaxillary protrusion and class II malocclusion is the most accurate for the nasolabial angle and the least accurate for the mandibular prognathism parameter.
INTRODUCTION: Wireless communication has made remarkable progress, by the rapid development of wireless technology in Artificial Intelligence (AI). Intelligent Transportation Systems (ITS), and Vehicular Ad Hoc Networks (VANETs) have received significant attention to ensure safety. However, V2V communication in VANETs faces uncontrollable challenges due to frequent intermittent connectivity issues in infrastructure-less networks. Addressing these problems in both safety and non-safety applications is a complex task.OBJECTIVES: To mitigate the intermittent connectivity problems, a novel Sparse Network Computational Model (SNCM) was proposed.METHODS: Extensive simulations using MATLAB to analyze the impact of spatial-temporal variations under different traffic flow densities. We varied the sensitivity factor (λ) at different time intervals while maintaining a constant traffic density. RESULTS: The findings indicate that there is no need to increase λ beyond certain thresholds for each level of service. The simulation results provide valuable guidelines for designing sparse networks, effectively mitigating frequent intermittent disconnections. Simulation experiments revealed an optimal threshold for the sensitivity factor λ for each level of service. Increasing λ beyond certain thresholds did not yield significant improvements in mitigating disconnections in V2V communication.CONCLUSION: The results provide valuable insights and guidelines for designing sparse networks to enhance connectivity and address intermittent disconnection issues. This paper presents a groundbreaking endeavor, and therefore, direct comparisons with existing protocols to evaluate its overall performance are beyond the scope of this paper. Instead, the SNCM protocol is intended to set a standard for future researchers to benchmark their research contributions against.
The project aim was to develop an app that would enable the recording and monitoring of behaviour related to specific aspects of wellness, as well as support those aspects of wellness that are entertainment-related. Our main goal was to envision and develop an app with the well-being of users in mind. People’s moods can be improved upon or changed by music, with music and mental health tightly intertwined. Music is frequently used to complement or change an individual’s mood. While there are advantages to mood-appropriate music, it may cause us to remain in a depressed, angry, or nervous state. A survey was conducted to examine these aspects. After performing a lot of research and interviews in this area, we found 68% of those surveyed listen to music according to their mood or to change their mood. This inspired us to build an application that not only plays music but also recommends songs to users, eliminating the daily nuisance of selecting the right music, which can waste valuable time. As mental balance is an essential component of healthy existence in today's hectic world, to enhance the practicality of our app, as icing on the cake, we included an AI chatbot that not only converses with the user but also provides them with suitable advice on their concerns.
