International Journal of Pattern Recognition and Artificial Intelligence

Public computer education systems provide students essential opportunities to enhance computer literacy and information skills. However, the widespread adoption of online education technology exposes the field to several critical security risks. Threats, such as malware infections, data breaches, and other network intrusions, are all challenging the security of education systems, posing potential hazards to students’ personal information and even the entire teaching environment. To spur further work into specialized anomaly detection techniques for computer education, this paper presents an anomaly detection framework tailored for network services in computer education environments to safeguard these systems. Specifically, the proposed approach learns from large-scale online educational traffic data to classify the security state into five alert levels, enabling more granular anomaly detection and analysis. To assess their detection performance, deep learning and traditional machine learning algorithms are implemented and compared for multi-class intrusion classification. The results show that the proposed framework provides an effective security solution to bolster the integrity and stability of computer education systems against evolving network threats, enhancing threat intelligence to inform proactive security by detecting and characterizing anomalies through multilevel classification.

Cardiovascular diseases (CVDs) represent a significant health concern in the present era, with Electrocardiogram (ECG) serving as a crucial bio-signal for their detection. Efficient health monitoring necessitates rapid and precise diagnosis, thereby mandating the utilization of Compressive Sensing (CS) alongside Machine Learning (ML) algorithms. CS functions as a sensing methodology that reduces sample numbers by capturing sparse or compressible representations, simplifying and expediting the acquisition process. In this proposed study, ECG signals are compressively sensed and preprocessed using CS reconstruction algorithms, followed by the application of various ML algorithms for diagnostic purposes. The assessment of the reconstructed ECG signal entails the assessment of Peak Signal-to-Noise Ratio (PSNR) values and Percentage Root-mean-square Difference (PRD). Concurrently, ML algorithms are evaluated based on metrics including accuracy, specificity, and sensitivity.

This work demonstrates exceptional performance in terms of acquisition time and computational complexity through the application of CS technology. Comparative analysis with the existing methodologies for CVD diagnosis reveals the proposed approach’s remarkable efficacy. Notably, the reduction in data volume and hardware complexity serves as a significant advantage over conventional methods. The integration of CS and ML algorithms in the proposed methodology proves highly effective in diagnosing CVDs, achieving a classification accuracy of 94.7%. These results underscore the methodology’s ability to deliver both speed and accuracy in diagnosis, positioning it as a promising approach for health monitoring.

The application of deep hashing methods for cross-modal retrieval has seen growing interest due to their storage efficiency and fast query execution. However, the challenge posed by the “heterogeneity gap” in multi-modal datasets cannot be understated. To address this, we present a novel framework named Dual-Pathway Deep Hashing-Based Adversarial Learning (DP-DHAL), engineered to surmount this challenge. The architecture of DP-DHAL integrates three key components: (a) a dual-pathway representation learning module tasked with extracting modality-specific features; (b) an adversarial module working to align the distributions of cross-modal features; and (c) a deep hashing module responsible for generating hash codes that uphold the similarity relationships across different modalities. Additionally, we have developed a unique Hamming triplet-margin loss function to refine the assessment of content similarities. The DP-DHAL model is trained through an adversarial process where the adversarial module’s goal is to discern cross-modal features with the aim of reducing the heterogeneity gap. Simultaneously, the representation learning module is focused on producing representations that can both deceive the adversarial module and preserve cross-modal similarities to yield distinctive hash codes. Comprehensive experiments on varied datasets have shown that our proposed method outperforms other leading cross-modal hashing techniques.

Deep learning classification is commonly used to assess biomedical data. Fetal heart rate (FHR) signal data monitor the health of both the mother and the fetus and prevent mobility and death in fetuses at risk of hypoxia. But, the data imbalance is a major problem and happens frequently. To overcome this problem, a Deep Siamese domain adaptation convolutional Neural Network optimized with Honey Badger Algorithm (HBA) is proposed in this paper for Fetal Arrhythmia Detection and Classification from ECG signals (DSDACNN-HBA-FAC). The input ECG signals are collected from the fetal ECG dataset. Then, the ECG data are pre-processed using coherence shock filtering (CSF), which eliminates artifacts, like baseline drift, contact noise, muscle artifacts, electrode motion, electromyography artifacts. Using the improved non-subs sampled Shearlet transform (INSST) method, the pre-processed ECG data recover the mean, standard deviation, root mean square, skewness, kurtosis, zero-crossing, autoregressive coefficients, estimated entropy, and Higuchi’s fractal dimension. The DSDACNN classifier utilizes the features extracted to categorize the output as normal and arrhythmia. In general, DSDACNN does not agree on any optimization techniques to define the optimum parameters and to ensure accurate classification. Therefore, HBA is employed to optimize the DSDACNN weight parameters. The proposed method is implemented in MATLAB. The performance metrics, like sensitivity, precision, recall, f-measure, specificity, accuracy, computation time, RoC, and error rate are evaluated. The performance of DSDACNN-HBA-FAC achieves 3.101%, 7.12%, 7.73% high accuracy, 51.136%, 59.04%, 44.51% lower computation Time and 2.292%, 5.365%, 1.551% greater AUC compared with the existing techniques, like Fetal Arrhythmia Detection with Classification from ECG Signals: A NonInvasive Method (ANN-FAC), Semi-supervised active transfer learning for fetal ECG arrhythmia identification (DNN-FAC), fetal arrhythmia detection using adaptive single channel electrocardiogram extraction (CNN-FAC), respectively.

Speech emotion recognition (SER) methods rely on frames to analyze the speech data. However, the existing methods typically divide a speech sample into smaller speech frames and label them with a single emotional tag, which fails to consider the possibility of multiple emotion tags coexisting within a speech sample. To deal with this limitation, we present a novel approach called self-labeling learning ensemble via DRNN and self-representation (En-DRNN-SR) for SER. This method automatically segments speech sample into speech frames, and then the deep recurrent neural network (DRNN) is applied to learn the deep features, and next the self-representation is built to get a relational degree matrix, finally the speech frames is divided into three parts using a relational degree matrix: the key emotional frames, the compatible emotional frames and the noise frames. The emotion tags of the compatible emotional frames are adaptive cyclic learned based on the key emotion frames vias the relational degree matrix, while also checking the emotion tags associated with the key compatible frames. Additionally, we introduce a new self-labeling criterion based on fuzzy membership degree for SER. To evaluate the feasibility and effectiveness of the proposed En-DRNN-SR, we conducted extensive experiments on IEMOCAP, EMODB, and SAVEE database, the proposed En-DRNN-SR obtains 69.13%, 82.83%, and 52.31% results on IEMOCAP, EMODB, and SAVEE database, which outperformed all competing algorithms. The experimental results clearly demonstrate that the proposed approach outperforms state-of-the-art SER methods, achieving superior performance on feature learning and classification.

The integration of artificial intelligence (AI) and deep learning heralds a transformative era in pattern recognition and computer vision, notably in image style transfer. We introduce the hierarchical dynamic multi-attention cycle generative adversarial network (HDMA-CGAN), an innovative deep learning architecture poised to redefine image style transfer capabilities. HDMA-CGAN employs a novel multi-attention mechanism and color optimization strategies, enabling precise style replication with improved fidelity and vibrancy. Our model surpasses existing benchmarks in image quality, validated by leading metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and Fréchet inception distance (FID). Although HDMA-CGAN advances the state of the art, it necessitates high computational resources and faces challenges with very high-resolution images. Future work could explore optimizing the model’s efficiency for real-time applications and extending its application to video content. This work enhances the tools available for visual content creation and digital media enhancement, leveraging advanced pattern recognition and AI techniques to significantly impact computer vision and image processing.

Accurate analysis and prediction of carbon prices are crucial. Nevertheless, their complexity and high volatility pose significant prediction challenges. To address this, we introduce a novel hybrid prediction framework, combining decomposition algorithms with neural networks. Our framework’s core lies in dynamically adjusting data input using the Cochrane–Orcutt procedure, based on the prediction autocorrelation error of component series, enhancing accuracy. To validate its effectiveness, we conducted a comprehensive study using EEX–EUA data, integrating various decomposition algorithms and prediction models. Experimental results reveal that our framework outperforms other algorithms in predicting carbon price.

With the increasing scale of mobile cellular network applications, 5G mobile network infrastructure provides customizable services to users in the form of network slices. How to effectively allocate existing resources in real dynamic networks with time-varying network utility is a key issue that previous work did not consider. This paper first initializes the multi-resource allocation problem of network slicing in an online manner, where the utility function is set to change over time. Therefore, we propose Metis, an online network slicing resource allocation framework that combines the time-varying nature of the network utility function given bandwidth and processing power constraints with the requirement of virtual network function isolation. The goal is to maximize the cumulative network utility in the long term and specify multiple resource allocation problems by utilizing concave optimization methods. In addition, a distributed algorithm based on the online alternating direction method of multipliers with regret optimization has been developed to achieve optimal resource allocation. Our mathematical analysis proves that Metis can provably converge to the optimal solution and the result of experiments demonstrates a steady state behavior of Metis, which converges in dynamic network settings.

This paper investigates the correlation between a pilot’s brain electroencephalographic (EEG) activity and his driving posture, addressing the intricate relationship between cognition and behavior in aviation. We designed and implemented a simulated experiment that recorded the fly pilot’s attitude and collected EEG information as experimental data. The experiment is only based on EEG data to predict flight posture (pull, down, left, right). We propose a flight-driving attitude prediction model (CA-FAP) based on CEBRA and self-attention mechanism, with a prediction accuracy of 0.83. This model is better than common spatial pattern (CSP) and uniform manifold approximation and projection (UMAP) dimension reduction methods in the experiment. Moreover, a better effect can be obtained in the larger attitude radian dataset (accuracy is 0.85), and the effect is not obvious in the dataset of closing the six-axis motion platform, indicating that the model prediction of flight attitude is closely related to the pilot position transformation. By comparing the prediction effect of each category, the pull-up and drop are better than the steering prediction result. The study can help pilots adjust their posture and decisions, and serve as a basis for studying flight pilot cognitive load, mental load, mood changes, and flight performance.

Multi-interest sequential recommendations leverage users’ historical behavior to provide recommendations that match multiple interests. Most of these methods have not fully extracted higher-order information hidden in users’ interactions and have overlooked the multiple features of items. To this end, this paper proposes a multi-interest model called “multi-interest sequential recommendation with simplified graph convolution and item multi-features (SGCMF)”. Firstly, a simplified graph convolution module is designed based on bipartite graphs, which utilizes mean pooling to aggregate neighboring information and employs a feedforward neural network (FNN) for nonlinear transformations and combinations. This method reduces redundant information and captures higher-order relationships, thereby simplifying the complexity of modeling high-order interactions and improving prediction accuracy. Secondly, an item multi-feature extraction module is proposed, which represents item features with multiple vectors, and analyzes each feature from multiple perspectives while preserving important relationships between features. The model correlates multiple features of the item with user interests, thereby achieving a fine-grained analysis of user interests. Extensive experiments are conducted on five real-world scenarios, and the results are compared with state-of-the-art methods. The experimental results show that SGCMF outperforms other baselines.