Deepfakes in video conferences pose an emerging threat, which demands real-time, multimodal, and privacy-preserving defense solutions. In this paper, we present an application of our argument from before in a different guise, stating that a centralized defense mechanism is insufficient because it lacks capabilities in spatial-temporal reasoning, audiovisual fusion, and handling privacy issues. To fill these limitations, this paper proposes a trans-DefendVC mechanism. The strategy begins with a series of multimodal preprocessing steps such that facial regions undergo alignment, cropping, patch embedding, and tokenization, with log-mel spectrogram patches being used for synchronized feature extraction, and two encoders are used on both modalities, where region-adaptive vision transformers address facial irregularities, with frequency-aware audio spectrogram transformers addressing speech manipulation. To model learning relationships between audiovisual signals, a cross-modality transformer attention module learns lip and speech synchronization at a high level of detail. A causal temporal transformer module addresses short- and long-term temporal dependencies necessary for real-time analysis. To support safe and efficient implementation, Federated transformer learning with differential privacy is used to support decentralized learning without sharing audiovisual signals. An integrated classifier informs authenticity decisions based on a unified representation, effectively identifying deepfake artifacts in space, spectral, and temporal domains. Experiments conducted on FakeAVCeleb and Celeb-DF v2 datasets prove efficacy with accuracy of 94.96/99.84
Cloud computing (CC) providers face a significant challenge in efficiently utilizing resources because system functionality and efficiency are directly impacted by optimal task scheduling. Scalability and efficiency are challenging to achieve because of the nonpolynomial time complexity of these problems and the complexity of scheduling virtual machines (VMs) in dynamic cloud environments. Conventional approaches frequently fall short, which raises the possibility of resource overloading or underuse, which can result in system failures. This research proposed a novel method for task scheduling in cloud environments, called the task-optima scheduling framework, to address these issues. To handle the heterogeneity, independence, and nonpreemptive nature of the tasks, this framework uses a sequential scheduling technique to execute multiple tasks on the same virtual machine. While virtual machines are sorted in descending order, tasks are prioritized and arranged in ascending order. To optimize job scheduling on virtual machines (VMs), a cloud broker employs a hybrid spider monkey optimization-simulated annealing (SMO-SA) algorithm. The main optimization method is SMO, and SA is occasionally applied to balance extraction and exploration in the solution space. The schedule is continuously improved through this iterative process, which lasts until a predetermined termination criterion is satisfied. By avoiding local optima, the hybrid approach reduces complexity and improves the search process by being flexible enough to adjust to changes in workload. The result reveals the proposed method achieved a makespan of 120 s, a computation time of 6 ms, and a Success rate of 98
Software-defined networking (SDN) coupled with the Internet of Things (IoT) offers a tremendous amount of flexibility and room for expansion, but this integration also results in new security vulnerabilities. A control-theoretic hybrid cyber defense framework is proposed in this work combining long short-term memory (LSTM) networks and model-agnostic metalearning (MAML) for adaptive attack detection, alongside Adaptive addax optimization algorithm (AAOA) for feature extraction and secure hash algorithm (SHA-256) for definite attack prevention. The suggested framework has been tested using four well-known datasets, NSL-KDD, CIC-IDS-2017, UNSW-NB15, and KDD Cup 99, and it has shown excellent results, even better than the current leading models, e.g. RNN, GRU, CNN, DNN, and autoencoder. Experimental results demonstrate F1-scores of 99.990, 99.995, 99.996, and 99.991
This paper proposes a novel renewable energy integration system combining solar energy using a packed U-cell (PUC) inverter regulated by a type-2 fuzzy logic controller (FLC) and advanced Maximum Power Point Tracking (MPPT) algorithms. The system incorporates a modified boost converter for efficient energy management and enhanced power output stability. The Type-2 FLC ensures robust control of the PUC inverter, effectively handling uncertainties and variations in the input energy sources, leading to reduced harmonic distortion and improved system performance. The advanced modified beluga whale optimization (MBWO) MPPT algorithm dynamically track the maximum power points of PV under fluctuating environmental conditions. Simulation and experimental validation confirm the system’s capability to achieve high energy efficiency, stable power delivery, and enhanced reliability, making it a promising solution for sustainable energy applications. From the obtained results, it is evident that the proposed system exhibits a lower THD about 2.85
In response to the severe impact of energy consumption and carbon emissions on the ecological environment in the current logistics transportation process, a system dynamics-based ecological logistics model is developed based on the logistics transportation system of province H in China. Firstly, the current situation of the logistics industry in province H is analyzed, and the parameters and system boundaries of the ecological logistics model are determined. Secondly, ecological logistics models related to highways, railways, and waterways are constructed. Verification showed that the maximum difference between the ecological logistics model proposed in the study and the historical value simulation of province H was only 8.37
