The 3D hand skeletal data has received considerable amount of attention due to its potential uses case for Hand Gesture Recognition (HGR) systems. Additionaly, Graph Neural Network (GNN) have been widely employed for skeletal-based HGR. Yet still, traditional models frequently suffer from inefficient feature representation and generalizability. Thus, to overcome these limitations, we introduce Spatial Graph Isomorphism Neural Networks (S-GINs), which use GIN layers to improve the feature aggregation. First, we create a graph representing skeletal-based recordings. Following that, a spatial network convolution module learns the inherent topology of hand gestures from neighbor nodes and updates it using a multilayer perceptron. This method promotes classification accuracy over other spatial based graph models including graph attention network, graph sampeling and aggregating, and graph convolutional networks. We validate S-GINs employing three benchmark datasets: MMHGD, 2MLMD, and Multi-view Leap2 , both horizontal and vertical sub-datasets. Experimental findings show that our model outperforms state-of-the-art graph based methods, by elevating the accuracies to 72%, 81%, 85%, and 83% respectively .

Global warming has caused significant thermal changes worldwide, increasing electricity demand by 20% to 30% in recent years. This surge disrupts the balance between energy production and consumption, sometimes leading to power outages or blackouts in certain countries. To mitigate this issue, optimizing HVAC systems, which account for more than 40% of global energy consumption, is a key solution, relying on either traditional or AI-based approaches. Our study examines the impact of gender, particularly women’s responses, on thermal comfort prediction in industrial environments with a high female presence. Using the ASHRAE RP-884 and Scales Project datasets and applying the XGBoost model, we demonstrated that women’s responses achieve the highest prediction accuracy at 68.60%, compared to 62.01% for men and 57.97% for combined responses. Moreover, based on the analysis of responses from different genders in the dataset excerpt, the comfort range for men falls within that of women, suggesting that integrating only female responses into thermal comfort models could enhance HVAC control, productivity, and energy efficiency.

Transfer learning is considered an effective technique that enhances model performance by using knowledge acquired from a source dataset to tackle a similar task on a target dataset. This technique is particularly valuable in fields where labeled data is limited, such as thermal comfort prediction. In the Tunisian context, the lack of specific thermal comfort data for office spaces occupied by multiple people represents a major challenge to optimizing workplace environments. To address this, we introduce a transfer learning-based approach using ASHRAE RP-884 data from countries with similar climatic conditions. In fact, these data were purified based on climate conditions and selected for office-type buildings. Three transfer learning methods were evaluated using three models: a Deep GRU-BiGRU model, a Deep GRU model, and a BiGRU model. Our results present that the transfer learning approach based on the Deep GRU-BiGRU model achieves the highest accuracy, reaching 66.15% in thermal comfort prediction, outperforming the other methods.

Deploying drones with deep-learning capabilities for real-time object detection is a trendsetting strategy, particularly in dynamic environments such as Intelligent Transportation Systems. Drones, or Unmanned Aerial Vehicles, offer advantages including a wide field of view, cost-effectiveness, and operational efficiency, making them ideal for perception and surveillance in dynamic settings. However, drones face several challenges in terms of on-board computational capabilities, constraining the deployment of complex algorithms for aerial imagery analysis. In this paper, we exhaustively study the challenges of real-time object detection for drones, emphasizing the adaptability and effectiveness of deep learning-based detectors. We fine-tune and evaluate the six most used scaled versions of real-time object detectors for drone-imagery, using a benchmark, tailored for drone-captured data, to assess detection accuracy, inference speed, and computational efficiency. Our study proves that YOLOv5n excels in terms of inference speed at 454 FPS, but with lower precision, while YOLOv8n surpasses in precision but requires more resources and longer inference times. Larger versions show moderate accuracy improvement but demand increased computational resources and extended inference duration.

In Multi-Processor System-on-Chip (MPSoC) architectures equipped with shared-memory, caches have significant impact on performance and energy consumption. Indeed, if the executed application depicts a high degree of reference locality, caches may reduce the amount of shared-memory accesses and data transfers on the interconnection network. Hence, execution time and energy consumption can be greatly optimized. However, caches in MPSoC architectures put forward the data coherency problem. In this context, most of the existing solutions are based either on data invalidation or data update protocols. These protocols do not consider the change in the application behavior. This paper presents a new hybrid cache-coherency protocol that is able to dynamically adapt its functioning mode according to the application needs. An original architecture which facilitates this protocol's implementation in Network-On-Chip based MPSoC architectures is also proposed. Performances, in terms of speed up factor and energy reduction gain of the proposed protocol, have been evaluated using a Cycle Accurate Bit Accurate (CABA) simulation platform. Experimental results in comparison with other existing solutions show that this protocol may give significant reductions in execution time and energy consumption can be achieved.