Advances in computational intelligence最新文献

Cardiovascular diseases (CVDs) are the leading cause of global mortality, necessitating efficient and accessible diagnostic tools for early detection. While deep learning models have demonstrated high accuracy in arrhythmia classification, their deployment in resource-limited clinical settings remains challenging due to computational constraints. This study addresses this gap by developing and validating lightweight deep learning models specifically optimized for edge device deployment, enabling real-time ECG analysis in remote and under-resourced healthcare environments. Utilizing the MIT-BIH Arrhythmia Dataset, which includes 48 half-hour ECG recordings from 47 individuals, we trained and evaluated 2 models: a standard Convolutional Neural Network (CNN) and a computationally efficient Depth-Wise Separable Convolutional Neural Network (DWCNN). Preprocessing involved segmenting ECG recordings into individual heartbeats and addressing class imbalance through downsampling, resulting in a balanced dataset of 21,186 images across 6 arrhythmia types. The DWCNN achieved competitive diagnostic performance (precision: 0.97, recall: 0.97) while utilizing 75% fewer parameters than the standard CNN (8.45 M vs. 34.08 M) and 65% less memory (8.4 MB vs. 24.1 MB). Critically, we demonstrate successful deployment on the Intel Neural Compute Stick 2 (NCS2), a resource-constrained edge device, achieving inference times of 4.09 ms/241.91FPS on CPU and 9.05 ms/109.79FPS on the NCS2 platform. This practical demonstration of real-time arrhythmia classification on low-power edge devices represents a significant advancement toward accessible cardiac diagnostics in point-of-care settings, remote monitoring applications, and resource-limited healthcare facilities where centralized computing infrastructure or specialist expertise may be unavailable.

Diabetic retinopathy (DR) stands out as one of the most significant causes of treatable visual impairment and blindness worldwide. Hence, early detection coupled with timely intervention is crucial to prevent the disease from further progression. However, manually detecting DR through the use of retinal images is highly time-consuming, subjective, and often inaccessible in resource-limited settings. This research introduces a groundbreaking automated system for DR detection and severity classification. First, we collected the corresponding data and applied advanced preprocessing techniques such as resizing image, normalization, Gaussian blur, contrast limited adaptive histogram equalization (CLAHE), and image blending to improve model performance. Furthermore, we employed powerful deep learning (DL) architectures such as VGG, EfficientNet, DenseNet, ResNet50, and transformer models such as vision transformer (ViT), and swin transformer for feature extraction. The resulting features were then classified using robust machine learning algorithms, ensemble models, CNN models, and transformer-based models, including decision tree (DT), K-nearest neighbor (KNN), logistic regression (LR), support vector machine (SVM), random forest (RF), AdaBoost, and XGBoost, VGG, EfficientNet, DenseNet, ResNet50, vision transformer, swin transformer, and proposed a new hybrid technique that integrates KNN with random forest (KNN-RF). We tested the system with the appropriate key metrics: accuracy, precision, recall, and F1-score. Our findings show that the hybrid KNN-RF technique outperforms the others. Results point to promising possibilities for our approach in providing precise, scalable, and cost-effective diabetic retinopathy diagnoses in resource-scarce settings. This study emphasizes the importance of artificial intelligence in revolutionizing healthcare diagnostic processes and tackling essential global health issues.

Transfer function plays a crucial role in discretizing metaheuristic algorithms to solve combinatorial optimization problems. However, existing transfer functions not only have few classes, but their design methods also rely too much on the curve shape. Kepler optimization algorithm (KOA) is a novel metaheuristic algorithm that performs well in solving optimization problems on continuous domains, but cannot be directly applied to solve combinatorial optimization problems on discrete domains. In order to design more transfer functions and solve combinatorial optimization problems by KOA, this paper first proposes a practical method to construct transfer functions. From this, two new classes of transfer functions are given: A-shaped transfer functions and B-shaped transfer functions. Then, based on the transfer function, the first discrete Kepler optimization algorithm (DKOA) suitable for binary optimization problems is proposed. To verify the practicality of the new transfer functions and the efficiency of DKOA, DKOA is used to solve 0–1 knapsack problem and knapsack problem with a single continuous variable, respectively. Comparison with existing transfer functions and the state-of-the-art metaheuristic algorithms for solving these two problems shows that DKOA using A-shaped and B-shaped transfer functions is more competitive in terms of the ability to obtain optimal solutions, average performance and stability. This shows that the proposed new transfer functions are very practical, and the DKOA based on them is an effective metaheuristic algorithm for solving binary optimization problems.

Projecting China’s producer price index (PPI) for production materials yields early signals of inflationary pressures and cost dynamics influencing both national economic stability and international supply networks. Reliable PPI forecasts equip policymakers, market participants, and firms with the information needed to refine monetary policy, pricing decisions, and resource allocation. This study proposes an innovative forecasting architecture based on Gaussian process regression (GPR), whose hyperparameters are estimated via a Bayesian inference procedure, enabling the model to adapt in real time to latent market fluctuations and previously unobserved structural shifts. By integrating these evolving characteristics, our approach more accurately captures changes in China’s PPI trajectory. The empirical analysis relies on a monthly dataset spanning October 1996 to February 2025, covering multiple waves of regulatory reform, industrial evolution, and macroeconomic transformation. Validation is performed over an out-of-sample period from June 2019 through February 2025, producing a relative root mean square error of 0.1120%, a root mean square error of 0.1131, a mean absolute error of 0.0832, and a correlation coefficient of 0.99984. To the best of our knowledge, this represents the first application of a Bayesian-inference-parameterized GPR model to forecast China’s PPI for production materials. Beyond advancing the theoretical discourse on machine-learning-based price prediction, the methodology provides a flexible analytical framework applicable to analogous macroeconomic time-series forecasting challenges.

Automated guided vehicles (AGVs) are intelligent robotic systems that play a crucial role in applications, such as transportation, food delivery, and medical emergencies. One of the primary challenges in AGV deployment is achieving precise navigation to ensure task reliability, safety, and efficient battery consumption along predetermined routes. Vision-based methods have gained significant attention for their high performance in robot navigation. However, selecting the most effective algorithm with minimal sensor use remains an active area of research. This study introduces a novel and efficient fusion-based navigation method, termed the Extended Fusion 1 Method (EFM1), which integrates data from camera and infrared (IR) sensors. The system leverages feature-based algorithms, such as SIFT, ORB, FAST, SURF, BRISK, and BRIEF, to improve path-tracking accuracy. The main objective of this fusion approach is to enhance navigational precision and identify the most suitable algorithm for robust AGV path-tracking. The EFM1 method is simulated and validated using the CoppeliaSim (V-REP) simulator, incorporating the real-world dimensions of the previously developed AGV model, Hongma, via Python API. The simulation evaluates six feature-based algorithms across five distinct path types: circular, elliptical, spiral, figure-eight, and custom path. Performance is assessed in terms of maximum achievable speed, minimal path-tracking error, body orientation accuracy, and simulation time. Statistical analysis, including inferential techniques and post hoc tests, is used to interpret the results. The experimental findings demonstrate that the proposed EFM1 algorithm outperforms traditional vision-only approaches in effectively tracking all five path types, confirming its potential for reliable and efficient AGV navigation. The proposed EFM1 algorithm integrates vision and IR sensors, enhancing AGV navigation accuracy by up to 84% while using minimal sensors. It outperforms previous methods by delivering up to 22.2% faster simulation times, significantly reduced error rates (for example, ORB error decreased by 93%), and increased speed across five test paths. The comparison between investigated methods shows that FAST excels on dynamic paths with notable speed improvements, while SURF performs better on complex trajectories as confirmed by statistical analysis. Although EFM1 improves most metrics, body orientation changes increased, indicating a trade-off between agility and movement stability.

This paper explores the integration of hesitant bipolar fuzzy soft sets (HBFSS) into multi-criteria group decision-making (MCGDM), aiming to enhance decision precision and resolve uncertainties in expert evaluations. We introduce a novel decision-making framework that combines the root mean square deviation (RMSD) method with a credibility score, capturing both the proximity to ideal solutions and the consistency of expert opinions. The process is applied to a sustainable energy project selection problem, showcasing its efficacy in ranking alternatives such as solar farm, wind park, and hydroelectric plant. A comparative analysis with the existing model highlights the limitations of traditional approaches, including the failure to differentiate alternatives with similar scores and neglecting expert consistency. Our results demonstrate that the proposed RMSD-Credibility approach offers a more nuanced, consistent, and precise ranking, improving decision quality in complex, uncertain environments. This paper contributes to advancing decision-making under fuzzy and uncertain conditions by providing an innovative aggregation mechanism tailored to the challenges of real-world multi-criteria problems.

Predicting personal traits, particularly age group, gender, handedness, and hand(s) used, in the form of digital identity for smartphone users by analysing keystroke dynamics (KD) attributes is a challenging area. However, it has a variety of applications in e-commerce, e-banking, e-teaching/learning, e-exams, forensics, and social networking. The main bottleneck of this problem is addressing the imbalanced nature of KD datasets using conventional machine learning (ML) approaches. By their inherent nature, KD datasets are often imbalanced from various perspectives due to the non-uniformity of diverse user traits and their varied usage patterns. This study proposes a predictive model for both fixed and free-text modes, considering the effect of attached smartphone sensors. We adopt a score-level fusion of eXtreme Gradient Boosting (XGBoost) models on several balanced bootstrapped training samples to address the limitations of conventional approaches. This ensemble approach utilizes multiple bootstrapped training sets, where the class distribution in each set is equally balanced for more accurate and robust performance. Furthermore, we observe the positive impact of incorporating these prediction scores and labels with primary biometric attributes in KD-based user authentication and identification, both in static/entry-point and continuous/active security designs—a previously unanswered challenges. The predictive mechanism and its adaptation in unique KD-based designs, based on datasets collected from a considerable number of volunteers with diverse age groups, genders, professions, and education levels through a smartphone in a web environment, demonstrate the novelty of our approach.

Keyword spotting (KWS) is a critical component of voice-driven smart-device applications, requiring high accuracy, sensitivity, and responsiveness to deliver optimal user experiences. Given the always-on nature of KWS systems, minimizing computational complexity and power consumption is essential, particularly for battery-powered edge devices with constrained resources. In this paper, we propose a compact and highly efficient convolutional neural network (CNN) for edge-based KWS tasks, using the Google Speech Commands (GSC) V2 dataset for training and evaluation. Our model employs modified MobileNetV2 architecture, optimized via knowledge distillation from an ensemble of high-performing CNN models. Experimental results demonstrate that the proposed model achieves 94.48% accuracy on clean test data and significantly outperforms existing state-of-the-art edge models on challenging noisy test sets, reaching 86.38% accuracy. The proposed CNN maintains this superior performance with only 73.8K parameters and 19.5M floating-point operations (FLOPs)—approximately three times fewer FLOPs and substantially fewer parameters than previously reported edge-focused KWS models. Moreover, when evaluated on a realistic and challenging external Kaggle test set, the proposed model shows excellent generalization with 88.38% accuracy, surpassing baseline depthwise separable CNN (DS-CNN) approaches. Upon practical deployment on a widely used embedded computing platform, our optimized model achieved fast inference times between 11 ms and 14 ms per sample, outperforming existing baseline methods and confirming its suitability for real-time applications. This study highlights the successful integration of model compression techniques, including ensemble learning and knowledge distillation, to achieve breakthrough performance improvements in accuracy, robustness to noise, computational efficiency, and inference speed, thereby advancing the practical deployment of high-performance KWS solutions on resource-constrained edge devices.

A meme is a part of media created to share an opinion or emotion across the internet. Due to their popularity, memes have become the new form of communication on social media. However, they are used in harmful ways such as trolling and cyberbullying progressively due to their nature. Various data modelling methods create different possibilities in feature extraction and turn them into beneficial information. The variety of modalities included in data plays a significant part in predicting the results. We try to explore the significance of visual features of images in classifying memes. Memes are a blend of both image and text, where the text is embedded into the picture. We consider a meme to be trolling if the meme in any way tries to troll a particular individual, group, or organisation. We try to incorporate the memes as a troll and non-trolling memes based on their images and text. We evaluate if there is any major significance of the visual features for identifying whether a meme is trolling or not. Our work illustrates different textual analysis methods and contrasting multimodal approaches ranging from simple merging to cross attention to utilising both worlds’—visual and textual features. The fine-tuned cross-lingual language model, XLM, performed the best in textual analysis, and the multimodal transformer performs the best in multimodal analysis.

Developing accurate and robust prognostic prediction for classifying the risks of acute lymphoblastic leukemia (ALL) relapse is critical for patient treatment management and survival. However, the lack of clinical samples and linearity assumption remains a significant clinical challenge for achieving high accuracy for single-cell prognostics. Here, we explore the use of non-linear machine learning models with ex vivo sample perturbation as a data augmentation strategy to improve ALL relapse prediction. We hypothesize that treating each sample with ex vivo perturbation can be viewed as independent measurements, thus increasing the number of available observations for machine learning. We show that ex vivo sample stimulation combined with non-linear machine learning significantly improves the performance of ALL risk stratification from limited single-cell proteomic data.