International Journal of Intelligent Systems最新文献

With the rapid development of entity recognition technology, animal recognition has gradually become essential in modern society, supporting labour-intensive agriculture and animal husbandry tasks. Severe problems such as maintaining biodiversity can also benefit from animal identification technology. However, certain invasive recognition systems have resulted in permanent harm to animals, while noninvasive identification methods also exhibit certain drawbacks. This paper conducts a systematic literature review (SLR), presenting a comprehensive overview of various animal recognition technologies and their applications. Specifically, it examines methodologies such as deep learning, image processing and acoustic analysis used for different animal characteristics and identification purposes. The contribution of machine learning to animal feature extraction is highlighted, emphasising its significance for animal taxonomy and wild species monitoring. Additionally, this review addresses the challenges and limitations of current technologies, including data scarcity, model accuracy and computational requirements, and suggests opportunities for future research to overcome these obstacles.

The field of generative artificial intelligence (AI) is experiencing rapid advancements, impacting a multitude of sectors, from computer vision to healthcare. This paper provides a comprehensive review of generative AI’s evolution, significance, and applications, including the foundational architectures such as generative adversarial networks (GANs), variational autoencoders (VAEs), autoregressive models, flow-based models, and diffusion models. We delve into the impact of generative algorithms on computer vision, natural language processing, artistic creation, and healthcare, demonstrating their revolutionary potential in data augmentation, text and speech synthesis, and medical image interpretation. While the transformative capabilities of generative AI are acknowledged, the paper also examines ethical concerns, most notably the advent of deepfakes, calling for the development of robust detection frameworks and responsible use guidelines. As generative AI continues to evolve, driven by advances in neural network architectures and deep learning methodologies, this paper provides a holistic overview of the current landscape and a roadmap for future research and ethical considerations in generative AI.

The one-class classification (OCC) problem has always been a popular topic because it is difficult or expensive to obtain abnormal data in many practical applications. Most of OCC methods focused on monomodal data, such as support vector data description (SVDD) and its variants, while we often face multimodal data in reality. The data come from the same task in multimodal learning, and thus, the inherent structures among all modalities should be hold, which is called the consistency principle. However, each modality contains unique information that can be used to repair the incompleteness of other modalities. It is called the complementarity principle. To follow the above two principles, we designed a multimodal graph–regularized term and a sparse projection matrix–regularized term. The former aims to preserve the within-modal structural and between-modal relationships, while the latter aims to richly use the complementarity information hidden in multimodal data. Further, we follow the multimodal subspace (MS) SVDD architecture and use two regularized terms to regularize SVDD. Consequently, a novel OCC method for multimodal data is proposed, called the consistency and complementarity jointly regularized subspace SVDD (CCS-SVDD). Extensive experimental results demonstrate that our approach is more effective and competitive than other algorithms. The source codes are available at https://github.com/wongchuang/CCS_SVDD.

Electric bus transport, a popular mode of public transportation, offers punctual, safe, and comfortable services to passengers through the efficient and effective use of designated road space. The performance of electric bus transport systems depends largely on the design of proper locations of bus stops, with the consideration of passenger demands, waiting time, and traveling time. Optimal electric bus route planning can attract an increasing number of passengers and increase public transit services. Aiming to provide guidance for the electric bus route planning of developing cities, this study proposed an intelligent route planning method to minimize the waiting time and traveling time of passengers, in order to achieve the best comfortable level. In addition, a self-learning anomaly detection method based on reinforcement learning (RL) was proposed to eliminate abnormal data caused by traffic accidents or emergencies. With a large spatiotemporal dataset collected over 3 years from a real electric bus project in Yantai, China, we developed a prototype system and conducted extensive experiments to evaluate the proposed intelligent route planning method. The results showed that the proposed method can reduce the passengers’ waiting time and attract more passengers traveling by electric bus. In addition, the proposed method has achieved optimal route planning recommendation (RPR) subject to 1,872,391 passenger demands on electric bus services; more than 86% of them were accurately predicted, and more than 97% were satisfied with recommendation results.

During the COVID-19 pandemic, the stay-at-home and biotechnology economies played a big part in economic development. The major internet forums have received more attention and discussions concerning stocks related to biotechnology and the stay-at-home economy. When the number of confirmed COVID-19 cases in a country rises, so does the positive or negative sentiment in stock commentaries. Whether stock forums can be a valuable source of information for investors has become a subject of academic research. This study used text mining and sentiment analysis to analyze stock forum articles, classify daily reports into emotion and investment orientation indicators, and correlate these indicators with the next-day 2019’s stock prices. The findings indicate a positive correlation between stock forum articles and stock prices. Additionally, this research enriched the case of sentiment analysis in the context of Chinese sentiment. This study contributes not only to academic reference and refinement but also to market investors’ judgment.

Aiming at the shortcomings of the current face liveness detection attack methods in the low generation speed of adversarial examples and the implementation of white-box attacks, a novel black-box attack method for face liveness detection named as FLDATN is proposed based on adversarial transformation network (ATN). In FLDATN, a convolutional block attention module (CBAM) is used to improve the generalization ability of adversarial examples, and the misclassification loss function based on feature similarity is defined. Experiments and analysis on the Oulu-NPU dataset show that the adversarial examples generated by the FLDATN have a good black-box attack effect on the task of face liveness detection and can achieve better generalization performance than the traditional methods. In addition, since FLDATN does not need to perform multiple gradient calculations for each image, it can significantly improve the generation speed of the adversarial examples.

Brain tumors cause death to a lot of people globally. Brain tumor disease is seen as one of the most lethal diseases since its mortality rate is high. Nevertheless, this rate can be diminished if the disease is identified and treated early. Recently, healthcare providers have relied on computed tomography (CT) scans and magnetic resonance imaging (MRI) in their diagnosis. Currently, various artificial intelligence (AI)-based solutions have been implemented to diagnose this disease early to prepare suitable treatment plans. In this article, we propose a novel self-attention transfer adaptive learning approach (SATALA) to identify brain tumors. This approach is an automated AI-based model that contains two deep-learning technologies to determine the existence of brain tumors. In addition, the proposed approach categorizes the identified tumors into two groups, which are benign and malignant. The developed method incorporates two deep-learning technologies: a convolutional neural network (CNN), which is VGG-19, and a new UNET network architecture. This approach is trained and evaluated on six public datasets and attained exquisite results. It achieved an average of 95% accuracy and an F1-score of 96.61%. The proposed approach was compared with other state-of-the-art models that were reported in the related work. The conducted experiments show that the proposed approach generates exquisite outputs and exceeds other works in some scenarios. In conclusion, we can infer that the proposed approach provides trustworthy identifications of brain cancer and can be applied in healthcare facilities.

Gravitational Search Algorithm (GSA) is a well-known physics-based meta-heuristic algorithm inspired by Newton’s law of universal gravitation and performs well in solving optimization problems. However, when solving high-dimensional optimization problems, the performance of GSA may deteriorate dramatically due to severe interference of redundant dimensional information in the high-dimensional space. To solve this problem, this paper proposes a Manifold-Guided Gravitation Search Algorithm, called MGGSA. First, based on the Isomap, an effective dimension extraction method is designed. In this mechanism, the effective dimension is extracted by comparing the dimension differences of the particles located in the same sorting position both in the original space and the corresponding low-dimensional manifold space. Then, the gravitational adjustment coefficient is designed, so that the particles can be guided to move in a more appropriate direction by increasing the effect of effective dimension, reducing the interference of redundant dimension on particle motion. The performance of the proposed algorithm is tested on 35 high-dimensional (dimension is 1000) benchmark functions from CEC2010 and CEC2013, and compared with eleven state-of-art meta-heuristic algorithms, the original GSA and four latest GSA’s variants, as well as three well-known large-scale global optimization algorithms. The experimental results demonstrate that MGGSA not only has a fast convergence rate but also has high solution accuracy. Besides, MGGSA is applied to three real-world application problems, which verifies the effectiveness of MGGSA on practical applications.

The simultaneous use of multiple drugs, known as polypharmacy, heightens the risks of harmful side effects due to drug-drug interactions. Predicting these interactions is crucial in drug research due to the rising prevalence of polypharmacy. Researchers employ a graphical structure to model these interactions, representing drugs and side effects as nodes and their interactions as edges. This creates a multipartite graph that encompasses various interactions such as protein-protein interactions, drug-target interactions, and side effects of polypharmacy. In this study, a method named PU-GNN, based on graph neural networks, is introduced to predict drug side effects. The proposed method involves three main steps: (1) drug features extraction using a novel biclustering algorithm, (2) reducing uncertainity in input data using a positive-unlabeled learning algorithm, and (3) prediction of drug’s polypharmacies by utilizing a graph neural network. Performance evaluation using 5-fold cross-validation reveals that PU-GNN surpasses other methods, achieving high scores of 0.977, 0.96, and 0.949 in the AUPR, AUC, and F1 measures, respectively.

We propose a novel model-free unsupervised learning paradigm to tackle the unfavorable prevailing problem of real-world image deraining, dubbed MUL-Derain. Beyond existing unsupervised deraining efforts, MUL-Derain leverages a model-free Multiscale Attentive Filtering (MSAF) to handle multiscale rain streaks. Therefore, formulation of any rain imaging is not necessary, and it requires neither iterative optimization nor progressive refinement operations. Meanwhile, MUL-Derain can efficiently compute spatial coherence and global interactions by modeling long-range dependencies, allowing MSAF to learn useful knowledge from a larger or even global rain region. Furthermore, we formulate a novel multiloss function to constrain MUL-Derain to preserve both color and structure information from the rainy images. Extensive experiments on both synthetic and real-world datasets demonstrate that our MUL-Derain obtains state-of-the-art performance over un/semisupervised methods and exhibits competitive advantages over the fully-supervised ones.