An Approach Based on Coloured Petri Net and NSGA-II to Improve the Emergency Department

Health care sector faces major challenges in light of the emergence of new diseases. The emergency department (ED) is an important area in the hospital, where it plays a major role in the presence of these challenges. The ‘ED’ is a complex system due to the random flow of patients and the complex nature of its resources. In this paper, the authors model and simulate the ‘ED’ by means of coloured Petri nets, and to determine the appropriate amount of resources, the NSGA-II algorithm is developed. After determining the appropriate amount of resources through the NSGA-II algorithm, the simulation model of the current system is modified with the amount of new resources obtained through the NSGA-II algorithm. The results are compared between the current system and the obtained system. This study was conducted in Hassani Abdelkader Hospital, located in the city of Sidi Bel Abbes, in western Algeria

Evaluating Combined Influence of Weighted Analysis Class Diagram Metrics on Early Software Size Estimation

Analysis class diagram (ACD) metrics like number of classes, number of methods, and number of attributes can be used for early software size estimation by project managers during initial project planning. However, not all of these ACD metrics have the same influence on software size. This study aims to empirically determine the relative influence of these ACD metrics on software size using historical data from academia and industry. Using the objective class points (OCP) metric as a base, two new metrics -- enhanced OCP (EOCP) and weighted EOCP (WEOCP) -- are proposed. Separate linear regression-based early software size estimation models are also constructed and validated using the original OCP metric and its newly proposed variants. A comparison of these models reveals that models based on our freshly proposed metrics perform better in terms of early size estimation accuracy

Lightweight Dual-Stream Human Behavior Inference Network Based on Multi-Layer Perceptron

The recognition of human behaviors in videos is a critical domain within human activity analysis. However, the current architectures and mechanisms of human behavior recognition methods dominated by CNN, GCNs, and LSTM are unduly complex resulting in high computational complexity of the models. Furthermore, these methods often exhibit poor robustness when it comes to recognizing behaviors across different environmental conditions and video angles. To address these challenges, this paper introduces a lightweight human skeleton interaction behavior inference network based on a multi-layer perceptron. This network leverages human skeleton information and utilizes minimal prior knowledge to infer limb behavior encoding. To reduce computational complexity, videos are divided into smaller segments, serving as the minimum computation units. This approach integrates three essential types of information: independent global information about individual postures, local interaction information regarding different limb parts, and temporal distance information. These three types of information are coupled through LSTM, incorporating temporal changes into network for recognition and classification. In comparison to previous similar methods, our proposed method is more lightweight, exhibits stronger robustness against interference and enables behavior recognition across different environments and perspectives

Collaborative Filtering Algorithm Based on Deep Denoising Auto-Encoder and Attention Mechanism

The burgeoning of e-commerce and online platforms has led to an explosion in data volume and diversity of user preferences, making effective recommendation systems crucial for personalizing user experiences. While collaborative filtering algorithms are traditionally favoured for their ability to leverage user-item interactions, they grapple with data sparsity and noise challenges. To tackle these challenges, Various approaches have emerged in recent years to tackle these challenges. Recent strides in deep learning, particularly autoencoders and neural networks, have shown promise in addressing these issues. However, limitations persist, such as suboptimal feature extraction and the underutilization of combined nonlinear and linear latent features in traditional autoencoders, as well as the overlooked impact of active users in recommendations. Addressing these research gaps, this study introduces a novel recommendation algorithm that synergizes a deep denoising autoencoder with an attention mechanism, aiming to refine recommendation performance by mitigating data sparsity and enhancing feature extraction. This fusion approach innovatively combines nonlinear and linear latent features and incorporates a neural attention mechanism, significantly improving the precision and personalization of recommendations. Ultimately, the proposed algorithm's effectiveness is assessed and benchmarked against state-of-the-art approaches, demonstrating its potential to revolutionize recommendation systems by offering more accurate and user-tailored suggestions

SDN-Based Multi-Objective Optimization for Task Offloading with Algorithm Federated Learning in Fog Computing Environment

Due to the substantial volume of data associated with the IoT, processing and storing such a large amount of data is not easily feasible. Nevertheless, many of its applications face challenges in cloud computing, such as latency, location awareness, and real-time mobility support. Edge computing helps provide solutions to these challenges. In this article, the MINLP path optimization problem is initially addressed using SDN, SA+GA, OLB-LBMM, and Round-Robin methods. Subsequently, based on the obtained results, the SDN method, which has achieved the best outcomes among the approaches, is selected. This article involves a simulation of the Internet of Things network for optimal allocation of shared resources in edge computing. The network architecture comprises five distinct layers, including cloud services, the SDN controller, edge computing nodes, edge computation and users. The algorithm employed in this problem is the federated learning and stochastic gradient descent algorithm. It selects the optimal edge node for user service provision through two learning and training phases, aiming to allocate shared resources with the goal of optimizing three parameters: cloud service providers' revenue, average latency, and user satisfaction. This algorithm is compared with several other methods. The selected model and algorithm, in comparison with other algorithms used in solving similar models, lead to a centralized management system, the implementation of effective network management, and the utilization of various communication media. This approach ensures timely access to services, contributing to increased profits for providers and user satisfaction

SMRFC-PDCNN: An Efficient Scene Matching Recognition with DCNN and Feature Clustering on Spark

Scene recognition, an AI technology based on deep learning, has been widely used in public safety, road traffic, and automatic driving, but applying it on massive data in deep convolutional neural networks (DCNNs) results in performance bottlenecks. This paper proposes SMRFC-PDCNN, an efficient scene matching recognition algorithm that addresses three specific problems: decreased accuracy of feature maps, redundant feature calculations, and low efficiency in parallel recognition. The proposed algorithm includes a feature pooling selection strategy called MI-IPSS, a feature selection strategy called DCPSO-FSS, a load balancing strategy called CCG-LBS. MI-IPSS solves the problem of de-creased accuracy of feature maps by adapting the pooling strategy based on mutual information coefficient between feature maps before and after pooling. DCPSO-FSS uses density clustering and particle swarm optimization to locate clustering parameters quickly and recognize clustered features through sampling in the fully connected layer. CCG-LBS dynamically calculates the computing overhead of feature maps and allocates data between groups according to the over-head to solve the problem of low efficiency in parallel recognition. Experimental results show that SMRFC-PDCNN has good performance and is suitable for the fast scene matching recognition process of parallelized DCNNs on large-scale datasets

MINet: A Pedestrian Trajectory Forecasting Method with Multi-Information Feature Fusion

Pedestrian trajectory prediction plays an exceptionally vital role in autonomous driving, enabling advanced analysis and decision-making in certain scenarios to ensure driving safety. Predicting pedestrian trajectories is a highly complex task, encompassing static scenes, dynamic scenes, and subjective intent. To enhance the accuracy of pedestrian trajectory prediction, it is crucial to model these scenarios, extract relevant features, and fuse them effectively. However, existing methods only consider some of the scenarios mentioned above and extract static scene features through manual annotation of road key points, which fails to meet the demands of autonomous driving in complex traffic scenarios. To overcome these limitations, this paper introduces MINet -- a network that employs multi-information feature fusion. Unlike previous approaches, MINet adopts a more automated approach to extract static scenes, including sidewalks and lawns. Moreover, the network incorporates pedestrian destination modeling to improve prediction accuracy. Furthermore, to tackle the challenge of collision avoidance in crowded spaces, this paper incorporates the extraction of dynamic scene changes through relative velocity modeling of objects. The proposed network achieved an improvement of 47.7 % in the ADE metric and 62.6 % in the FDE metric on the ETH/UCY dataset. In the SDD dataset, there was an improvement of 18.4 % in the ADE metric and 35.2 % in the FDE metric

DeliteSeg: A Real-Time Semantic Segmentation Model for Predicting Small Objects and Object Contours

Semantic segmentation is one of the key technologies in the development of autonomous vehicles. Practical applications are increasingly pursuing a balance between effectiveness and efficiency. Many lightweight segmentation models nowadays have some problems, often making it difficult to predict small objects and edges between different objects. In this work, we propose a model of encoder-decoder structure, DeliteSeg. Firstly, we added deformable convolutional layers to the encoder, leveraging the advantages of deformable convolution to enable the model to better predict object edges. Then we proposed a new deep context aggregation module DLPPM, which improves the context information aggregation ability by fusing low-resolution feature maps of different scales multiple times, enabling the model to better predict small objects. Finally, we designed a new lightweight attention decoder (LMD) that utilizes a spatial channel attention mechanism to refine feature maps at different levels, effectively recovering information. After extensive experiments, our network achieved 73.6 % mIou and 123.7 FPS on the Cityscapes dataset and 73.9 % mIou and 116.4 FPS on the CamVid dataset. The experimental results confirm that our proposed model can make appropriate trade-offs between accuracy and real-time performance

Efficient Drone Detection Method Based on YOLOv8s Improvement

Combating illegal drone activities is an important task for national defense and security. How to spot drones quickly and accurately is the key. While there are many ways to detect drones, their reasoning is generally slow and complex. Therefore, in this work, we propose an improved and efficient UAV detection method YOLOv8s-C3AS based on YOLOv8s. There are three main improvements to this approach: First, we propose a new Coordinate Channel Spatial Attention Module (CCSM) and add it to the backbone of the model to enable better feature extraction. Secondly, in order to solve the scale inconsistency problem of YOLOv8s PANet, we propose a new adaptive fusion feature network (PANet-AF), which enables the model to fuse the features of the three scales better, which enables the model to better integrate features of different scales. Third, we use a more reasonable bounding box regression loss function SIoU, which improves the detection accuracy of the model without cost. Finally, we refined and made public the drone dataset and conducted a series of experiments combined with the PASCCOL VOC dataset. Our proposed approach achieves 77.2 % mAP, 98.9 % mAP_50, 87.1 % mAP_75 and 120.5 FPS on the drone dataset. Experiments demonstrate that our proposed method outperforms other methods by achieving high detection accuracies while maintaining faster inference speed and lower model parameters. The drone datasets used for this research has been uploaded to kaggle: https://www.kaggle.com/datasets/zhangtutu123/drone-dataset123/dat

Self-Supervised Learning for 3D Action Prediction Based on Past Completeness and Future Trend

The goal of the 3D action prediction task is to predict the action label corresponding to an incomplete 3D skeleton sequence. Existing studies are limited to the supervised framework. To eliminate the dependence of supervised learning on expensive labels, we propose a self-supervised learning method for 3D action prediction. We use three self-supervised tasks of action completeness perception, motion prediction, and global regularization to allow the network to learn the past and future information embedded in the sequence of unfinished actions, i.e., the action completeness that has occurred and the future motion trend, and to optimize the feature space learned by the model. Some models ignore the past and future information embedded in partial sequences, which is the key to action prediction by humans. Based on our self-supervised method, we design two modules, an action completeness perceptron, and a motion predictor, to complete missing information in partial inputs. And a novel network structure is proposed to fuse partial and complete prediction to achieve more reasonable action prediction. We have conducted extensive experiments on different datasets, and the results validate the effectiveness of our proposed method