International Journal of Pattern Recognition and Artificial Intelligence

Fog computing is a type of distributed computing that makes data storage and computation closer to the network edge. While fog computing offers numerous advantages, it also introduces several challenges, particularly in terms of security. Intrusion Detection System (IDS) plays a crucial role in securing fog computing environments by monitoring network traffic and system activities for signs of malicious behavior. Several techniques can be employed to enhance intrusion detection in fog computing environments. Accordingly, this paper proposes a Shepard Neuro-Fuzzy Network (ShNFN) for intrusion detection in fog computing. Initially, in the cloud layer, the input data are passed to data transformation to transform the unstructured data into structured form. Here, data transformation is done employing the Box-Cox transformation. Following this, the feature selection is done in terms of information gain and symmetric uncertainty process and it is used to create a relationship between two variables. After that, the data are classified by employing the proposed ShNFN. The ShNFN is attained by fusing two networks, such as Cascade Neuro-Fuzzy Network (Cascade NFN) and Shepard Convolutional Neural Networks (ShCNN). After this, the physical process is executed at the endpoint layer. Finally, intrusion detection is accomplished in the fog layer by the proposed ShNFN method. The performance of the intrusion detection using ShNFN is calculated by the metrics of recall, F-measure and precision. The proposed method achieves the values of 93.3%, 92.5% and 94.8% for recall, F-measure, and precision, respectively.

The rapid development of modern society and continuous urbanization have resulted in a proliferation of functional buildings, which offer significant convenience to individuals, but pose significant fire hazards as well. How to detect the fire at the early stage is always the focus of research. This paper proposes a multi-information source fusion fire recognition method based on particle swarm optimization (PSO)-backpropagation (BP) neural networks and ResNet50. The PSO algorithm is applied to optimize the initial parameters of a BP neural network model, while data from three sensors — temperature, humidity and smoke — are integrated, through iterative training of the system, accurate recognition of sensor data can be achieved. Additionally, a method is proposed for the recognition of infrared fire images using ResNet50 and transfer learning. By improving the ResNet50 network model and migrating the ResNet50 pre-trained network weight, infrared fire image recognition accuracy is further enhanced. Then the sensor information recognition results and image information recognition results are input into the fuzzy system for fusion reasoning again, and the final decision is output according to the set fuzzy rules. Experimental findings demonstrate that the multi-information source fusion approach utilizing the PSO-BP neural network and ResNet50 significantly enhances the accuracy and response time of fire recognition, and achieves a remarkable recognition effect.

Scene text detection is a challenging topic in computer vision, characterized by complex illumination, irregular shape, and arbitrary size. While recent advancements have been made in scene text detection, it remains difficult to simultaneously distinguish nearby text and accommodate irregularly shaped text. Therefore, this paper introduces HPNet, an enhanced text detector, based on the segmentation method that predicts two-scale results. To improve the shape robustness, the Hybrid Attentional Feature Fusion (HAFF) module is integrated into Feature Pyramid Networks (FPN) to dynamically perform feature fusion. Additionally, to distinguish nearby text, the model predicts the text region covering text instances and the text kernel covering the central region of the text. The improved Pixel Aggregation (PA) algorithm is then utilized to guide the expansion from the text kernel to the text region. Experiments on IC15, Total-Text, and CTW1500 validate the effectiveness of these improvements and the superiority of HPNet. Compared with the previous method PSENet for nearby texts, the proposed HPNet has improved inference speed by 63.6% and F-measure metric by 2.6%, 3.7%, and 2.5% on three datasets, respectively.

Cyberbullying, a widespread issue in digital communication, involves using online platforms to harass or demean individuals. Addressing it effectively requires understanding its manifestations across different linguistic contexts. This study presents a novel approach to cyberbullying detection, exploring its manifestations in seven languages through two distinct research paradigms: monolingual and multilingual scenarios. The monolingual approach focuses on developing and testing detection models within a single language framework. In contrast, the multilingual approach, which has shown superior performance, integrates data from multiple languages to train a unified model. This innovative strategy aims to harness broader linguistic diversity and enhance the model’s generalizability. We utilized three computational models: SONAR$+$DNN, MUSE$+$CNN-BiLSTM, and XLM-RoBERTa, with the SONAR$+$DNN architecture demonstrating the most effective performance. This model combines SONAR’s sentence-level embeddings with the nuanced understanding of DNN, making it particularly adept at handling the complex variations of cyberbullying across languages. Our results indicate that multilingual models perform better, particularly in languages with significant representation, such as Arabic and English. Our evaluation shows that our models consistently outperform the best-recorded results on seven diverse datasets, achieving superior performance in six. This significant achievement underscores the robustness of our approach and marks an essential advancement in cyberbullying detection.

In this paper, we propose a new algorithm called LL-Diff, which is innovative compared to traditional augmentation methods in that it introduces the sampling method of Langevin dynamics. This sampling approach simulates the motion of particles in complex environments and can better handle noise and details in low-light conditions. We also incorporate a causal attention mechanism to achieve causality and address the issue of confounding effects. This attention mechanism enables us to better capture local information while avoiding over-enhancement. We have conducted experiments on the LOL-V1 and LOL-V2 datasets, and the results show that LL-Diff significantly improves computational speed and several evaluation metrics, demonstrating the superiority and effectiveness of our method for low-light image enhancement tasks. The code will be released on GitHub when the paper has been accepted.

In order to obtain super-resolution images with richer details and clearer textures, a method for image super-resolution reconstruction using dense residual attention and multi-scale fusion is proposed. First, different scale convolutions are used to fully extract shallow features of the image; then high-frequency features of the image are extracted through one three-layer cascaded multi-scale feature fusion and dense residual attention module, and the reuse of feature map is achieved; finally, residual branches are used to introduce shallow features and high-frequency features of each channel image, and the high-resolution images are reconstructed through up-sampling and sub-pixel convolution. The test results on the Set5, Set14, Bsd100, and Urban100 datasets show that the PSNR and SSIM of our model are superior to most current algorithms, especially in the case of $\times 4$ reconstruction results. PSNR has improved by 0.2 dB on the Set5 and Bsd100 datasets, and the algorithm has a better subjective visual effect.

Facing the demand for accurate, fast, and natural human–computer interaction in the multi-screen control environment, the traditional exchange method of frequent switching control of multi-screen through mouse, keyboard, and other manual methods has problems of low efficiency, weak flexibility, and poor experience. In this paper, we research multi-screen precision control technology based on eye movement to break through the difficulties of precise recognition of human vision under complex environments and poor stability of vision estimation under frequent switching control of multi-screen. This study explores the technology for controlling multiple screens with high precision by tracking eye movements. It overcomes the challenges associated with reliably discerning human visual focus in intricate settings and the instability of visual assessments during rapid transitions between multiple screens. The experimental results of 1143 eye-movement capture tasks are studied, including the accurate recognition technology of human head posture and pupil gaze direction under complex backgrounds, illumination, and different visual field environments, and the theoretical modeling method of multi-screen precise target interaction based on eye-movement is explored. The expression data will be processed using convolutional neural networks, and the dataset will be trained through Python programming and tested on test samples collected by simulated flight crews in the laboratory with an accuracy rate of more than 85%.

Robotic grasping techniques for regular targets with known shapes are now well established. However, unknown shaped objects have complex features such as texture, shape, and appearance, which leads to inaccurate recognition and localization of shaped objects during grasp detection. To improve the generalization ability of the grasping detection network for unfamiliar shaped objects, we propose a lightweight shaped object grasping detection network (LSOGD) based on feature fusion, which solves the problem that the network repeatedly extracts features from images and ultimately improves the accuracy of model detection by combining different features. The effectiveness of LSOGD is confirmed by performance evaluation on the Cornell dataset and Jacquard dataset, where the detection accuracy reaches 97.9% and 96.7% for unknown objects, respectively. In addition, due to the small proportion of shaped objects in the current publicly available dataset, we added a portion of industrial-shaped pieces based on the selection of some shaped objects in the Cornell grasping dataset to build a shaped object dataset named X-Cornell on which the accuracy of our proposed model for grasping and detecting the unknown shaped objects is 94.6%. Finally, an actual robot grasping experiment was conducted using a Realsense d435i camera and a Kinova robotic arm, and the success rate of grasping shaped objects was 94%.

The application of convolutional neural network has been gradually introduced in the field of X-ray bone images. At present, there is less research on methods to automatically detect abnormal parts of the skeleton. The method improves on the Densenet network by adjusting the network structure, adding a convolutional block attention module (CBAM) attention mechanism to the original Densenlayer, fusing spatial attention and channel attention to suppress unnecessary features and strengthen the network‘s capability to extract image features, and optimizing the Transition block by fusing two pooling strategies, average and maximum, to increase the model’s anti-interference capability. The data collected after the experiment show that the improved Densenet has increased accuracy in the detection of skeletal abnormalities at all sites compared with the traditional network. The average accuracy is 1–5% higher than the benchmark method, the highest elbow accuracy reached 77.62%, the lowest hand accuracy also reached 64.28% and the area under the receiver operating characteristic curve is increased by 3–7%, the highest reached 80.83%.

With the development of artificial intelligence, computer vision technology has been widely used in the fields of security monitoring, automatic driving and wisdom city. However, there has not been a research on the detection of the meridians in human bodies by using the computer vision technology. In order to promote the use of the computer vision technology in human meridian detection, this paper first releases a dataset based on human meridians, which makes up for the gap in the field of human meridian detection using image processing technology. Moreover, the human meridian detection dataset is manually annotated and proofread by experienced Traditional Chinese Medicine (TCM) practitioners according to the position and direction of the human meridians, so that the annotated human meridians are as accurate as possible. The released human meridian dataset label’s 12 meridians, including spleen meridian, pericardium meridian, stomach meridian, lung meridian, heart meridian, kidney meridian, gallbladder meridian, liver meridian, triple energizer meridian, bladder meridian, large intestine meridian and small intestine meridian. A total of 296 acupoints were labeled. At last, this paper proposes a method for data augmentation, especially for datasets with a small amount of data, wherein the data amount can be augmented by enhancing the underlying edge visual features of the data. Experimental results show that human meridians can be detected by using image processing technology, and the proposed method for data augmentation can effectively improve the detection accuracy of human meridians. The dataset can be downloaded from https://www.zksylf.com/col.jsp?id=127.

To address the issue of low accuracy in speech fatigue recognition (SFR) under small samples, a method for small-sample SFR based on generative adversarial networks (GANs) is proposed. First, we enable the generator and discriminator to adversarially train and learn the features of the samples, and use the generator to generate high-quality simulated samples to expand our dataset. Then, we transfer discriminator parameters to fatigue identification network to accelerate network training speed. Furthermore, we use a bidirectional long short-term memory network (BLSTM) to further learn temporal fatigue features and improve the recognition rate of fatigue. 720 speech samples from a self-made Chinese speech database (SUSP-SFD) were chosen for training and testing. The results indicate that compared with traditional SFR methods, like convolutional neural networks (CNNs) and long short-term memory network (LSTM), our method improved the SFR rate by about 2.3–6.7%, verifying the effectiveness of the method.

Discussions surrounding Maritime Autonomous Surface Ships (MASS) are occurring in academia, industry, and the International Maritime Organization (IMO). Automatic collision avoidance, as one of the key technologies of MASS, is the core of the autonomous navigation function of MASS, and its role is to solve the autonomous collision avoidance problem during MASS navigation. Convention on the International Regulations for Preventing Collisions at Sea (COLREGS) is a fundamental basis for automatic collision avoidance, and the navigation of MASS should adhere to it. However, the applicability of MASS to COLREGS has not yet been resolved. Responding to these issues, the paper proceeds to analyze the principal challenges of MASS to COLREGS in terms of the application of MASS to good seamanship, the neglect provision, the lookout provision, the insight of one another provision, and the problems of the deviation provision. Furthermore, suggestions are put forth for the revision of COLREGS, including the mode of revision, the reconstruction of MASS collision liability, the long-term coexistence of MASS and traditional ships, and the risk control of COLREGS revision in the context of artificial intelligence. These suggestions aim to establish a foundation for a more effective adaptation to the advent of the MASS era.