International Journal of Pattern Recognition and Artificial Intelligence

Online education has gained significant popularity during the COVID-19 pandemic. The availability of massive open online courses has further strengthened the online learning environment. One of the considerable challenges in online learning environments is to measure the learner’s engagement to meet the educational objectives. This paper addresses the challenge and proposes a convolutional neural network-based architecture that extracts discriminative features from the face (Affective) and the upper body (Behavioral) of the learner and classifies the engagement. The performance of the proposed architecture is evaluated on the publicly available Dataset of Affective States In E-Environments (DAiSEE) and the learning-centered affective state dataset curated from open-source datasets. The experimental results demonstrate that the proposed methodology with affective and behavioral features improves the engagement measurement results. The proposed method outperforms the state-of-the-art in terms of Unweighted Average Recall (UAR), Unweighted Average Precision (UAP), and Unweighted Average F1 (UAF1) with 79.14%, 49.62%, and 55.29% values, respectively. It is threefold more computationally efficient than the previous state-of-the-art method. The proposed method also improves the accuracy of the curated dataset by 2.07% of the prior method.

Nowadays, there is high commercial demand for product replacement which places the products virtually in Key Opinion Leader’s (KOL’s) social media videos. However, one of the challenges of placing the products virtually is the KOL segmentation. Since KOLs often hold products in front of them, it requires the segmentation to segment not only humans but also different products. This paper introduces the state-of-the-art deep learning method, namely RSUDISNet, for KOL segmentation. The proposed technique integrates two deep Convolutional Neural Network (CNN) technologies. One is the Matting Objective Decomposition Network (MODNet), which segments KOLs well but not the products blocking the KOLs. The other one is the two-level nested U-structure network (U2Net) based on the salient object detection method to segment the objects well, but not the KOL. The key technique of the proposed research is to employ the feature of the U2Net to embed the MODNet to overcome the problem of KOL segmentation. Since both MODNet and U2Net are lightweights, the combined network can be used for real-time scenarios. After that, the Intermediate Supervision (IS) training strategy is utilized to overcome the overfitting. The experimental results show that our proposed method outperforms the MODNet and U2Net.

Most fine-grained visual recognition methods endeavor to directly locate discriminative regions in intricate environments, but tend to overlook the object’s holistic structure, which may lead to misclassification due to overemphasizing incorrect areas. In this paper, we propose a coarse-to-fine paradigm, which prioritizes locating holistic structural regions of the target object, followed by a gradual search to locate discriminative areas. Specifically, we first design the “look into object” module to locate the areas encompassing the target’s holistic structure using prior information. Subsequently, without introducing additional parameters, we design a partial focus searching module to enhance feature representations of discriminative regions within the target’s structural composition. Ultimately, we segregate the foreground components from the original image, attaining a more precise characterization of the target. Furthermore, we demonstrate the practical application potential of our model in real-world industries through our self-constructed DHU-Fine-grained-6000 dataset. Comparative experiments on three public datasets indicate that the superiority of our approach over many recent methods and holds promising application potential in industrial production processes.

Text spotting in person and vehicle re-identification images is complex due to the presence of multiple views of the same person and vehicle. Most existing models focus on text spotting in natural scene images, our work focuses on spotting in person and vehicle re-identification images. The rationale behind this work is that the person and the vehicles share symmetry properties and the bib number in the torso and license plate number in the vehicle are text. The method divides the input image into patches, and it explores vision transformation for encoding the patches into linear patches. The linearly embedded patches are fed to the feature similarity index step, which involves phase congruency and gradient magnitude to detect symmetric patches. The transformer is proposed to encode and capture textual information from the symmetry patches for text detection and recognition. The decoder receives the attention features from the encoder and fetches a multi-task head with the information about the detected and recognized text. The experiments on person and vehicle image benchmark, viz. (Person) Re-ID, RBNR, UFPR-ALPR and RodoSol datasets show significant improvement in performance when compared to other text spotting models. The effectiveness of the proposed model is validated by testing on the benchmark datasets, namely, ICDAR 2015, Total-Text and CTW1500 of natural scene images. Furthermore, cross-data validation shows the proposed method is independent of domains.

Wood lumber is widely used in the construction and furniture manufacturing industries. In order to solve the problems of poor recognition, low efficiency and few detection types of manual and traditional processing methods, this paper proposes a model SGM-YOLO for the detection of surface defects in wood lumber. The SGM-YOLO model references a new backbone feature network, SL-backbone, to enhance the model’s ability to detect defects of different sizes. And, a new GVE-neck layer structure will be proposed in this paper, which reduces the parameters as well as the accuracy. In addition, the Normalized Weighted Distance Loss (NWD) small target detection algorithm is combined with the MPDIOU boundary loss function to replace the original loss function to further enhance the small target detection capability. Experiments show that SGM-YOLO achieves an average recognition accuracy of 77.4% for wood lumber defects, compared with the original model YOLOv8, the mAP is improved by 3.8% and the FPS is improved by 4.4, while the number and size of parameters are reduced, which provides better detection of several defects that are difficult to be identified. The methods presented in this paper were also applied to the YOLOv5 model, yielding positive results, to confirm its generalizability. The results demonstrate the high application value of the SGM-YOLO model in the wood lumber processing and manufacturing industry’s final product inspection.

Vehicle re-identification primarily faces two challenges: significant intra-class variations caused by different camera views and subtle inter-class differences between vehicles of the same model. In this paper, we propose a Multi-View Progressive Graph Interaction Embedding Network (MP-GIEN) for vehicle re-identification. First, we design a global semantic extractor. By employing Graph Interaction Units (GI Units) and semantic context, we combine semantic information to facilitate contextual reasoning of image regions, thereby extracting global semantic features of the target. The global loss is then calculated using ID loss and triplet loss. Second, we devise a local view-aware extractor. The U-Net network is utilized to parse the image into four views (front, rear, top, and side). Subsequently, features are aligned through mask average pooling, and a progressive training strategy is adopted to address the learning of fine-grained information. Lastly, we design a feature fusion enhancer. We revise the typical triplet loss to avoid mismatches in local features. By optimizing the local triplet loss and global loss, we learn the embedding of visual features, which not only reduces the intra-instance distance but also enlarges the inter-instance differences. MP-GIEN facilitates the capture of stable discriminative information on vehicles under different views. Experiments conducted on the VeRi-Wild dataset demonstrate that our model significantly outperforms state-of-the-art methods.

This paper proposes a channel pruning method based on multi-stage feature de-correlation to obtain a more efficient convolutional neural network (CNN) model. Based on the correlation of hidden features at each level of the network, we refine more efficient features of each convolutional layer by applying feature de-correlation constraints (MFD Loss) to each convolutional layer of the network and then prune channels according to the modulus of the feature maps output from each layer. After several rounds of pruning and fine-tuning, a network with similar accuracy, a substantially smaller network size, and more efficient operation is generated compared to the original model. Our experiments on pruning various popular CNN models on many standard datasets demonstrate the method’s effectiveness. Specifically, for VGG-16 on CIFAR10, our approach eliminates parameters by 97.0%, saves Float-Point-Operations (FLOPs) by 66.9%, with a 0.4% accuracy gain and state-of-art performance. For ResNet-50 on the ImageNet dataset, our method eliminates parameters by 30.0%, and saves FLOPs by 52%, with 1.4% accuracy loss, which also proves the effectiveness of the method. The code for the paper can be found at https://github.com/lovelyemperor/MFD.

With the rapid growth and widespread use of low-end commercial unmanned aerial vehicles (UAVs), it is critical to develop an object detection system that works well with these devices. This paper designs an efficient and lightweight object detection model specifically designed for low-end UAVs. Through excellent information interaction and the refined use of some techniques, our model can obtain multi-scale features and better focus on the details of different parts. Extensive experiments show that our model still maintains comparable accuracy while it consumes fewer parameters and FLOPs. In addition, our model has been applied to the tracking system of low-end UAVs, greatly enhancing the tracking performance.

A common vascular disease called peripheral arterial disease (PAD) is characterized by constricted or blocked arteries in the legs, which lowers blood flow and raises the risk of consequences including cardiovascular events or amputation of a limb. Timely intervention and management of PAD are contingent upon early identification. Atherosclerosis, or plaque accumulation in the arteries, is the main cause of this illness. Therefore, leg pain, cramping, numbness, weakness, or coolness in the legs are possible signs of PAD, especially when engaging in physical activity. In addition to impairing mobility and quality of life, PAD raises the possibility of consequences like tissue damage, persistent sores, and even limb amputation if treatment is not received. Mixes of dietary adjustments, medication, and, in extreme situations, surgical procedures are used to manage PAD. A nutritious diet, frequent exercise, giving up smoking, and keeping a healthy weight are a few examples of lifestyle changes. It may be necessary to prescribe pharmaceuticals to decrease cholesterol, antiplatelet agents, blood flow improvers, and symptom managers. Procedures like angioplasty, stenting, or bypass surgery may be required in cases when PAD progresses and symptoms are severe to restore blood flow to the affected limbs. To avoid issues and enhance results, PAD must be identified early and managed effectively. As a result, those who are at risk such as those with diabetes, high blood pressure, high cholesterol, or a history of smoking should have screening for PAD regularly. All things considered, PAD is a dangerous illness that needs to be managed proactively to avoid complications and preserve a high standard of living. Through the management of risk factors, adoption of a healthy lifestyle, and adherence to an individualized treatment plan, people with PAD can effectively manage their illness and lower their chance of complications. For long-term treatment and best results, regular monitoring and follow-up with healthcare specialists are necessary. Vascular feature extraction boosting (VFEB), a hybrid algorithm that combines the strength of vascular feature extraction with boosting approaches, is the unique strategy for PAD identification that is present in this study. The VFEB approach makes use of gradient boosting machines (GBMs) to improve classification performance based on these features and convolutional neural networks (CNNs) to extract discriminative features from vascular imaging data. The goal of VFEB is to increase the precision and resilience of PAD detection by combining CNNs for feature extraction with GBMs for classification. Using a dataset of vascular imaging data, we assess the VFEB algorithm’s performance and compare it with other established techniques. The findings show that VFEB performs better than other methods at identifying PAD, providing encouraging opportunities for the early detection and treatment of this crippling vascular ailment. The VFEB system achieves an outstanding accuracy of 99.87%, an F1 Score of 99.64, and MCC 99%. Compared to the conventional methods, VFEB obtains improved accuracy, F1 Score, and MCC by integrating boosting techniques with vascular feature extraction. This study emphasizes the value of utilizing cutting-edge computational tools to improve vascular health outcomes and shows how hybrid algorithms, such as VFEB, may improve PAD identification.

Fault classification of mechanical equipment is a vital issue in modern industrial production. Mechanical equipment typically relies on multiple sensors to collect the operational data, which is represented as multivariate time-series data. However, existing methods for analyzing mechanical faults often overlook the causal relationships between sensors and struggle with the scarcity of labeled training samples. To address these challenges, we propose a graph neural network model leveraging sensor causality and meta-learning for mechanical fault classification (SCML-GNN). Specifically, we use transfer entropy to represent multivariate time-series data as a graph, with each sensor as a node and their causal relationships as edges. We then extract the node features using temporal convolutional layers and apply a graph neural network to learn the low-dimensional features. Additionally, graph pooling methods are used to obtain global embeddings. To further tackle the issue of limited labeled training samples, we introduce a metric-based class prototype attention mechanism within SCML-GNN. Extensive experiments conducted on three real-world mechanical equipment datasets demonstrate the superior effectiveness and efficiency of SCML-GNN in mechanical fault classification compared to the other existing methods.

Medical information system, like the Internet of Medical Things (IoMT), has gained more attention in recent decades. Disease diagnosis is an important facility of the medical healthcare system. Wearable devices become popular in a wide range of applications in the health monitoring system and this has stimulated the increasing growth of IoMT. Recently, a smart healthcare system has been more effective, and various methods have been developed to classify the disease at the beginning stage. To capture the patient’s information and detect the disease, a new framework is designed using the developed Conditional Auto regressive Mayfly Algorithm (CAMA)-based Deep Residual Network (DRN). Initially, pre-processing is done by the T2FCS filtering technique to increase the image quality by eliminating noises. The second step is segmentation. Here, the segmentation of brain tumor is done using U-Net. After that, data augmentation is performed to enhance image dimensions using the techniques, such as flipping, shearing, and translation to solve the issues of data samples. After processing the data augmentation mechanism, the next step is brain tumor detection, which is done using DRN. Here, DRN is trained by the proposed CAMA, which is the integration of conditional auto regressive value at risk (CAViaR) with the mayfly algorithm (MA). The developed model reduces computational complexity and increases effectiveness and robustness. The proposed CAMA-based DRN outperformed with an utmost testing accuracy of 0.921, sensitivity of 0.931, specificity of 0.928, distance of 52.842 and trust of 0.697.

In this study, the failure of a certain type of gearbox of a high-speed locomotive group with cracks is examined, and a gearbox failure assessment method that considers the coupled vibration is established and combined with mechanical signal and frequency signal to determine the basis for judging the failure of a faulty gearbox. First, according to the mechanical model of the finite element calculation, we determined the stress weak links and then the layout response stress measurement points and acceleration measurement points. We then calculated the gearbox ratio, meshing frequency, vibration frequency, mechanical response, modal response, and other frequency characteristics using the Hilbert–Huang transform (HHT) method and the fast Fourier transform (FFT) algorithm to analyze the vibration signals generated by different speeds and wheel out-of-roundness conditions. These were used to calculate the frequency of the different vibration sources of the mechanical response on the weak areas. The frequency correlations of the different vibration sources on the mechanical response in the weak areas were then analyzed, and the vibration transmission law of the gearbox case was obtained. The fault determination criterion was then determined, and the final cause of the fault was obtained.

Network anomalies significantly impact the efficiency and stability of network systems, making effective anomaly detection crucial for optimal performance and prevention of network breakdowns. However, conventional methods must be improved for handling anomalies’ complexities and evolving nature. Despite extensive research in network anomaly detection (NAD) techniques, there is a need for more systematic literature reviews incorporating recent advances, particularly in dynamic and heterogeneous network settings. Moreover, most review papers focus on individual detection methods, needing a unified framework for comprehensive anomaly detection. To bridge these gaps, this paper conducts a comprehensive analysis by conducting a systematic literature review and formulating five research questions to outline the objectives of this study. A holistic framework is proposed, integrating techniques based on preprocessing and Feature Selection into prediction models to develop more accurate, efficient, and reliable anomaly detection systems. The empirical evaluation assesses the effectiveness, accuracy, efficiency, and reliability of the data-driven NAD techniques. Finally, the study identifies research gaps and potential future directions to guide further advancements in developing accurate and efficient anomaly detection models. By synthesizing and analyzing 116 top-cited papers, this study contributes to the existing body of knowledge by highlighting the potential of emerging anomaly detection techniques in complex and dynamic network environments.