ABSTRACT The increasing number of automobiles on the road has worsened traffic, raised questions about safety, and had an adverse effect on the environment. Traffic Signal Control (TSC) has become a key strategy to address these issues. While conventional TSC systems help manage urban traffic, their reliance on fixed schedules and simplified models limits their effectiveness in complex, real‐world conditions. In order to overcome the above shortcomings, this paper introduces an Improved Twin‐Actor Twin‐Delayed Deep Deterministic Policy Gradient (ITATD3) algorithm designed for traffic signal control at single intersections. The main innovation of the ITATD3 proposed is in two aspects: (1) the incorporation of a spatial occupancy‐based reward which in combination with waiting time, better measures road‐space utilization and congestion than traditional metrics, and (2) the application of the Cheetah Optimization Algorithm (COA) for fine‐tuning of key hyper parameters of the ITATD3 model to improve convergence speed and stability. The performance of the proposed model is compared against four benchmark models, like TATD3, TD3, D3QN, and DQN. Results show that ITATD3 performs consistently better than other models on various performance metrics, such as travel delay, average waiting time, traffic conflicts, spatial occupancy, throughput, and average travel time. The proposed ITATD3 reduced travel delay by a maximum of 68% and improved traffic throughput significantly compared to conventional models. These results reveal the benefits of ITATD3 as a viable solution for real‐time data‐driven traffic signal control in intelligent transportation systems.

Multispectral images (MSI) and hyperspectral images (HSI) classification present significant challenges due to their high spectral dimensionality and redundancy among adjacent bands. In this paper, a Hierarchical Adaptive Self-Attention Guided Ensemble Band Selection Approach (HASGEBSA) is proposed. It is an ensemble of statistical distance, dependence measures, with an explainable AI interpretability to identify the most discriminative and optimal spectral bands. After selecting the top-kmost optimal and informative spectral bands,k-channel images are constructed that preserve the spatial structure and spectral redundancy. Then these images are fed into a lightweight kchannel convolutional model that contains a pre-trained VGG16 backbone to exploit both spectral and spatial features. The experiments are conducted on the Indian Pines (IP) and EuroSAT datasets. The results show that HASGEBSA achieves classification accuracy of 99% and 96%, respectively. Additionally, the proposed model is lightweight with only ~7M trainable parameters, which shows its significant improvements in terms of computational efficiency.

Efficient recognition of suspicious activity in surveillance videos requires reliable temporal modeling under strict computational constraints. Although uniform binarization significantly reduces computational complexity, it often disrupts temporal feature continuity, which is critical for video-based anomaly detection. This letter proposes a temporal-aware partially binarized vision transformer (TAPBViT) that introduces a signal driven precision allocation strategy for spatiotemporal transformers. The proposed method models intermediate representations as temporal feature signals and assigns computational precision according to temporal sensitivity, preserving full precision tempo ralattention layers while binarizing spatial projection and feed forward components that exhibit lower temporal distortion. This selective quantization mitigates quantization-induced temporal degradation while enabling aggressive computational reduction. Experiments on the UCF-Crime and UCSD Ped2 datasets demon strate that TAPBViT achieves accuracies of 92.67% and 98.72%, respectively, while reducing binary operations by up to 10× and supporting real-time inference on edge hardware. These results establish temporal signal-guided precision allocation as an effective principle for efficient transformer-based video surveillance.

The complexity of traffic flow patterns significant challenges in predicting traffic green signal timings using conventional methods. Most of conventional methods relied on vehicle counts and speeds. These methods often did not consider crucial factors such as Spatial Occupancy, long-term dependencies, and the non-linear relationships. Recent advancements in Convolutional Neural Networks (CNNs) have enabled better capturing of patterns in traffic data. These advancements are essential for effectively predicting vehicle Green Signal Time by considering accurate detection and tracking, Spatial Occupancy calculation, long-term dependencies, and non-linear relationships in traffic data. The PVD-GSTPS framework has been proposed as an innovative solution for predicting vehicle Green Signal Time with the help of advanced CNN. This framework leverages the capabilities of two fine-tuned object detection models YOLO v8 and Faster R-CNN for precise vehicle detection, while a Byte Sort Tracker monitors the trajectories of detected vehicles. Additionally, a vehicle counting module assesses the number of vehicles in specified areas, and a size assignment process estimates Green Signal Time based on Spatial Occupancy calculations. This study is limited by the fixed duration of the QMUL video dataset utilized. This restricts data availability and complicates the establishment of strong correlations between Green Signal Time and Spatial Occupancy. To mitigate this issue, we utilized a Generative Adversarial Network (GAN) to generate realistic synthetic data. Long Short-Term Memory (LSTM) networks and polynomial regression techniques are utilized to capture the relationships within this dataset. In this study, we used the QMUL dataset to validate our hypothesis. The results demonstrate that our PVD-GSTPS framework significantly outperforms Enhanced YOLO v8, Original YOLO v8, and Faster R-CNN.

Image captioning generates text in natural language to describe a given image. Recent advances in various object detection with attention mechanisms pushed for exploring multiple image captioning methodologies to create more meaningful and accurate captioning models. Although existing pipelines do well in describing the image, there has not been enough emphasis on relationship modeling between image features. Relationship modeling is crucial for establishing context between various objects in an image. We have proposed a novel Advanced Context-Aware Object Relational Model (ACAORM) that not only improves relationship modeling between image features but also better sentence generation due to Transformer architecture. ACAORM builds relation-aware visual representations for image description and builds captions with prior attention to relevant Regions of Interest (RoI). We tested the proposed methodology with three widely used datasets, Flickr8K, Flickr30K, and MS-COCO. The results show that it beats numerous cutting-edge techniques. ACAORM scores 0.3526, 0.4439, and 0.8813 in $$ BLEU-4 $$ on Flickr8k, Flickr30k and MS-COCO, respectively, outperforming popular cutting-edge models such as VitaCap on Flickr8k and AGF on Flickr30k and MS-COCO by 10.88%, 47.96% and 139.48% on the respective benchmarks.