An efficient self-adjusting flexible metaheuristic is introduced to develop automatic voltage and load frequency regulation schemes for multi-area power system. It employs non-linear threshold accepting algorithm to find the best combinations of PID control gains using innovative nonlinear search function. This approach outperformed well established heuristics and other conventional internal model control approaches in terms of the dynamic response, robustness in uncertain dynamical environments, and quality of the solutions.

The automation of surveillance systems, driven by the rapid development of computer vision technology, has significantly enhanced the analysis of surveillance videos, particularly in recognition of human activity, including behavior analysis and violence detection, thereby bolstering public and industrial security. Despite these advancements, detecting and analyzing violent actions remains challenging, especially for real-time surveillance systems with limited computing power. We propose an artificial intelligence-based framework called VD-Net (Violence Detection Network), enabled by Intelligent Internet-of-Things (IIoT) to detect violent behavior in public and private spaces. The model utilizes lightweight special task temporal convolutional network (ST-TCN) blocks and several bottleneck layers to focus on salient features in the input sequence. The learned features passed from the classifier to discriminate between violent and nonviolent actions. Additionally, our system is supposed to trigger an alert if violence is detected, which is then communicated to relevant departments. We tested the effectiveness of the proposed system by conducting experiments on surveillance and non-surveillance datasets and ensured a 1-4 % improvement in State-of-The-Art (SoTA) accuracy.

The field of routing and sensor networking is an important and challenging research area of network computing today. Advancements in sensor networks enable a wide range of environmental monitoring and object tracking applications. Routing in sensor networks is a difficult problem: as the size of the network increases, routing becomes more complex. Therefore, biologically-inspired intelligent algorithms are used to tackle this problem. Ant routing has shown excellent performance for sensor networks. In this paper, we present a biologically-inspired swarm intelligence-based routing algorithm, which is suitable for sensor networks. Our proposed ant routing algorithm also meet the enhanced sensor network requirements, including energy consumption, success rate, and time delay. The paper concludes with the measurement data we have found.

The field of Multimodal Emotion Recognition (MER) has made considerable advancements in recent years; however, the opportunity to leverage the synergistic relationships between different modalities remains largely untapped. This paper introduces an MER approach employing a Joint Multi-Scale Multimodal Transformer (JMMT) with recursive cross-attention for naturalistic recognition of emotions by enhancing and capturing inter-and intra-modal relationships across both (visual and audio) modalities. We compute multi-scale attention weights based on cross-correlations between multi-scale joint representations of combined and individual cues to capture inter and intra-modal dynamics. As a result of individual modalities, recursive inputs are fed back during the fusion for further refinement of features. Our JMMT model presents a cost-effective solution for consumer devices by capturing synergistic characteristics across visual and audio inputs. The JMMT model outperforms the state-of-the-art (SOTA) methods in MER systems, which were evaluated by IEMOCAP and MELD datasets.