International Journal of Intelligent Systems最新文献

The rapid development of vehicle-to-vehicle (V2V) communication technology provides more opportunities to improve traffic safety and efficiency, which facilitates the exchange of multivehicle information to mine potential patterns and hidden associations in vehicle trajectory prediction. To address the importance of fine-grained vehicle interaction modelling in vehicle trajectory prediction, the present work proposes an integrated vehicle trajectory prediction model that combines the multihead attention mechanism with long short-term memory (IMA-LSTM) in multivehicle interaction scenarios. Compared to existing studies, a dedicated feature extraction module including both individual features and interactive features is designed and sophisticated multihead attention mechanism is applied with LSTM framework to capture the variation of spatial-temporal interactions between vehicles. The performance of the proposed model in different scenarios is examined using both the high-D and the NGSIM dataset through comprehensive comparison experiments. The results indicate that the proposed IMA-LSTM model presents great improvement in vehicle trajectory prediction performance in different scenarios compared to the model that does not consider multivehicle interaction features. Moreover, it outperforms other existing models in 3–5s prediction horizons and such superiority is more evident in left lane-changing (LLC) scenarios than lane-keeping (LK) and right lane-changing (RLC) scenarios. The outcomes fully address the importance of fine-grained interactive feature modelling in improving vehicle trajectory performance in complex multivehicle interaction scenarios and could further contribute to more refined traffic safety and traffic efficiency management.

Electroencephalography (EEG) data serve as a reliable method for fatigue detection due to their intuitive representation of drivers’ mental processes. However, existing research on feature generation has overlooked the effective and automated aspects of this process. The challenge of extracting features from unpredictable and complex EEG signals has led to the frequent use of deep learning models for signal classification. Unfortunately, these models often neglect generalizability to novel subjects. To address these concerns, this study proposes the utilization of a modified deep convolutional neural network, specifically the Inception-dilated ResNet architecture. Trained on spectrograms derived from segmented EEG data, the network undergoes analysis in both temporal and spatial-frequency dimensions. The primary focus is on accurately detecting and classifying fatigue. The inherent variability of EEG signals between individuals, coupled with limited samples during fatigue states, presents challenges in fatigue detection through brain signals. Therefore, a detailed structural analysis of fatigue episodes is crucial. Experimental results demonstrate the proposed methodology’s ability to distinguish between alertness and sleepiness, achieving average accuracy rates of 98.87% and 82.73% on Figshare and SEED-VIG datasets, respectively, surpassing contemporary methodologies. Additionally, the study examines frequency bands’ relative significance to further explore participants’ inclinations in states of alertness and fatigue. This research paves the way for deeper exploration into the underlying factors contributing to mental fatigue.

The liver is the largest organ of the human body with more than 500 vital functions. In recent decades, a large number of liver patients have been reported with diseases such as cirrhosis, fibrosis, or other liver disorders. There is a need for effective, early, and accurate identification of individuals suffering from such disease so that the person may recover before the disease spreads and becomes fatal. For this, applications of machine learning are playing a significant role. Despite the advancements, existing systems remain inconsistent in performance due to limited feature selection and data imbalance. In this article, we reviewed 58 articles extracted from 5 different electronic repositories published from January 2015 to 2023. After a systematic and protocol-based review, we answered 6 research questions about machine learning algorithms. The identification of effective feature selection techniques, data imbalance management techniques, accurate machine learning algorithms, a list of available data sets with their URLs and characteristics, and feature importance based on usage has been identified for diagnosing liver disease. The reason to select this research question is, in any machine learning framework, the role of dimensionality reduction, data imbalance management, machine learning algorithm with its accuracy, and data itself is very significant. Based on the conducted review, a framework, machine learning-based liver disease diagnosis (MaLLiDD), has been proposed and validated using three datasets. The proposed framework classified liver disorders with 99.56%, 76.56%, and 76.11% accuracy. In conclusion, this article addressed six research questions by identifying effective feature selection techniques, data imbalance management techniques, algorithms, datasets, and feature importance based on usage. It also demonstrated a high accuracy with the framework for early diagnosis, marking a significant advancement.

Recently, live streaming technology has been widely utilized in areas such as online gaming, e-healthcare, and video conferencing. The increasing network and computational resources required for live streaming increase the cost of content providers and Internet Service Providers (ISPs), which may lead to increased latency or even unavailability of live streaming services. The current research primarily focuses on providing high-quality services by assessing the resource status of network nodes individually. However, the role assignment within nodes and the interconnectivity among nodes are often overlooked. To fill this gap, we propose a hierarchical game theory-based live video transmission framework to coordinate the heterogeneity of live tasks and nodes and to improve the resource utilization of nodes and the service satisfaction of users. Secondly, the service node roles are set as producers who are closer to the live streaming source and provide content, consumers who are closer to the end users and process data, and silent nodes who do not participate in the service process, and a non-cooperative game-based role competition algorithm is designed to improve the node resource utilization. Furthermore, a matching-based optimal path algorithm for media services is designed to establish optimal matching associations among service nodes to optimize the service experience. Finally, extensive simulation experiments show that our approach performs better in terms of service latency and bandwidth.

This article deals with the use of software-defined networking (SDN) in the Industrial Internet of Things (IIoT). The use of SDN in IIoT can solve the limitations presented by traditional networks in order to guarantee quality of service (QoS) for new applications. The approach of this work centers on the selection of an SDN controller that satisfies the requirements for the networks of IIoT. Selection is based on the characteristics of the SDN controllers and employs the analytic hierarchy process (AHP). From the review conducted, and as a result of the work, the group of the current best SDN controllers for IIoT is identified, which is a part of the subsequent selection process. Another contribution of this study is that it defines the criteria for comparing these controllers and selecting the most suitable one for this type of application. The established criteria and the employed quantification method via AHP enrich the decision-making process, providing a replicable model for future selections. The objectives and criteria established can be useful for other SDN selection processes to be used in scenarios where delay, jitter, and packet loss are key parameters to consider. This nuanced approach, accommodating both theoretical frameworks and empirical observations, offers an advancement in the strategic deployment of SDN within IIoT environments.

Many-objective evolutionary algorithms (MaOEAs) consisted of environmental selection and reproduction operator. However, few studies focus on how to design reproduction operators to improve the performance of MaOEAs. In this paper, a reproduction operator based on grey prediction is proposed for MaOEAs, named GPRS. Specifically, the grey prediction assisted by reference vector is first used to get the target location. Then, a fine regulation is designed to generate potential solutions by handling the different information further. Finally, a gene sharing strategy is executed to accelerate the convergence by information exchange. The effectiveness of the proposed reproduction strategy is validated by comparing it with five widely used reproduction operators by embedding into a classical framework NSGAIII. At the same time, an improved NSGAIIIGPRS is developed by embedding the proposed GPRS and compared with seven excellent algorithms on a number of benchmark problems and one practical application. The final experimental results show that the proposed GPRS has significant advantages over similar reproduction strategies, and the improved NSGAIIGRPS is more effective compared with other excellent algorithms in handling many-objective optimization problem.

Facial expression recognition (FER) has a wide range of applications, including interactive gaming, healthcare, security, and human-computer interaction systems. Despite the impressive performance of FER based on deep learning methods, it remains challenging in real-world scenarios due to uncontrolled factors such as varying lighting conditions, face occlusion, and pose variations. In contrast, humans are able to categorize objects based on both their inherent characteristics and the surrounding environment from a cognitive standpoint, utilizing concepts such as cognitive relativity. Modeling the cognitive relativity laws to learn cognitive features as feature augmentation may improve the performance of deep learning models for FER. Therefore, we propose a cognitive feature learning framework to learn cognitive features as complementary for FER, which consists of Relative Transformation module (AFRT) and Graph Convolutional Network module (AFGCN). AFRT explicitly creates cognitive relative features that reflect the position relationships between the samples based on human cognitive relativity, and AFGCN implicitly learns the interaction features between expressions as feature augmentation to improve the classification performance of FER. Extensive experimental results on three public datasets show the universality and effectiveness of the proposed method.

As well known, the positivity is an essential topic when studying gene regulatory networks since the variables involved, e.g., the concentrations of mRNA and proteins, can never be negative. However, the positivity of Caputo fractional order models has been a longstanding problem due to the nonlocality of Caputo fractional derivatives (CFD). In this paper, we present the system comparison method to prove the positivity of Caputo fractional order gene regulatory networks (CFOGRNs) only under positive initial conditions. Moreover, it is found that the positivity results can make it feasible to give proper comparison systems for CFOGRNs, in which the upper and lower estimations can be used to guarantee the stability of the objective CFOGRNs. Thus, the system comparison method for the stability of CFOGRNs is also provided here. Compared to the existing Lyapunov direct method, the proposed system comparison method affords an alternative method for stability analysis and different insights in stability conditions. Finally, these theoretical derivations are illustrated and validated by an example with numerical simulations.

In colorectal cancer (CRC), accurately predicting lymph node metastasis (LNM) contributes to developing appropriate treatment plans and serves as the key to long-term survival of patients. In the clinical settings, preoperative LNM diagnosis in CRC predominantly depends on computed tomography (CT). Nevertheless, lymph nodes are small in size and difficult to identify on 3D CT scans, and CT-based diagnosis of metastatic lymph nodes is prone to a significant misdiagnosis rate and lacks consistency across clinicians. Currently, there is no automatic system available for LNM prediction in CRC via 3D CT scans. In addition, existing deep learning- (DL-) based lymph node detection models present low detection accuracy and high false-positive rates, and most existing DL-based lymph node metastasis prediction models mainly use tumor area characteristics but fail to adequately utilize lymph node information, thus not yielding satisfactory results. To tackle these issues, we propose an intelligent diagnosis system for this challenging task, mainly including a lymph node detection (LND) model and a lymph node metastasis prediction (LNMP) model. In detail, the LND model utilizes an encoder-decoder network to detect lymph nodes, and the LNMP model employs an innovative attention-based multiple instance learning (MIL) network. An instance-level self-attention feature enhancement module is designed to extract and augment lymph node features as a bag of instances. Furthermore, a bag-level MIL prediction module is employed to extract instance features and create a bag representation for the ultimate LNM prediction. As far as we know, the proposed intelligent system represents the pioneering method for addressing this complex clinical challenge. In experiments, our proposed intelligent system achieves the AUC of 75.4% and the accuracy of 73.9%, showcasing a significant enhancement compared to physicians specialising in CRC and highlighting its strong clinical applicability. The accessible code can be found at https://github.com/SCU-MI/IS-LNM.

The advancement of intelligent systems, particularly in domains such as natural language processing and autonomous driving, has been primarily driven by deep neural networks (DNNs). However, these systems exhibit vulnerability to adversarial attacks that can be both subtle and imperceptible to humans, resulting in arbitrary and erroneous decisions. This susceptibility arises from the hierarchical layer-by-layer learning structure of DNNs, where small distortions can be exponentially amplified. While several defense methods have been proposed, they often necessitate prior knowledge of adversarial attacks to design specific defense strategies. This requirement is often unfeasible in real-world attack scenarios. In this paper, we introduce a novel learning model, termed “immune” learning, known as adversarial-resilient deep symbolic tree (ARDST), from a neurosymbolic perspective. The ARDST model is semiparametric and takes the form of a tree, with logic operators serving as nodes and learned parameters as weights of edges. This model provides a transparent reasoning path for decision-making, offering fine granularity, and has the capacity to withstand various types of adversarial attacks, all while maintaining a significantly smaller parameter space compared to DNNs. Our extensive experiments, conducted on three benchmark datasets, reveal that ARDST exhibits a representation learning capability similar to DNNs in perceptual tasks and demonstrates resilience against state-of-the-art adversarial attacks.