Artificial Intelligence Review最新文献

This paper proposes a novel genetic algorithm to optimize recommendations from multiple trading strategies derived from the Directional Changes (DC) paradigm. DC is an event-based approach that differs from the traditional physical time data, which employs fixed time intervals and uses a physical time scale. The DC method records price movements when specific events occur instead of using fixed intervals. The determination of these events relies on a threshold, which captures significant changes in price of a given asset. This work employs eight trading strategies that are developed based on directional changes. These strategies were profiled using varying values of thresholds to provide a comprehensive analysis of their effectiveness. In order to optimize and prioritize the conflicting recommendations given by the different trading strategies under different DC thresholds, we are proposing a novel genetic algorithm (GA). To analyze the GA’s trading performance, we utilize 200 stocks listed on the New York Stock Exchange. Our findings show that it can generate highly profitable trading strategies at very low risk levels. The GA is also able to statistically and significantly outperform other DC-based trading strategies, as well as 8 financial trading strategies that are based on technical indicators such as aroon, exponential moving average, and relative strength index, and also buy-and-hold. The proposed GA is also able to outperform the trading performance of 7 market indices, such as the Dow Jones Industrial Average, and the Standard & Poors (S&P) 500.

The degradation of concrete structures is significantly influenced by carbonation, where atmospheric carbon dioxide (CO₂) penetrates the concrete matrix. Measuring how far carbonation penetrates into concrete plays a vital role in maintaining structural integrity and construction safety standards. Precisely forecasting the extent of carbonation penetration in recycled aggregate concrete (RAC) remains fundamental for understanding long-term performance and durability. This research is the first to introduce an innovative approach that leverages eight machine learning algorithms to estimate carbonation penetration depth. The selected techniques include NGBoost, GBRT, AdaBoost, CatBoost, XGBoost, LightGBM, HistGBRT, and MLR. Moreover, to evaluate model accuracy, four key performance indicators were employed. Additionally, SHapley Additive exPlanations (SHAP) was incorporated for enhanced model interpretability. Furthermore, the investigation examined six distinct input parameter configurations during training and testing to thoroughly assess model performance. Among the evaluated algorithms, XGBoost delivered the highest accuracy, with an RMSE of 1.389 mm, MAE of 1.005 mm, and R of 0.984. CatBoost followed closely, with RMSE of 1.772 mm, MAE of 1.344 mm, and R of 0.976. Then, the LightGBM ranked third in effectiveness, exhibiting an RMSE of 1.797 mm, MAE of 1.296 mm, and R of 0.975. These results demonstrate the reliability and interpretability of advanced machine learning models for carbonation depth estimation in RAC. The developed models offer practical tools for engineers seeking to evaluate how carbonation penetration affects structural integrity. These findings establish a strong foundation for understanding and predicting carbonation-related deterioration in concrete infrastructure.

AutoML represents a pivotal advancement in machine learning by simplifying and speeding model development. This paper provides a comprehensive survey of AutoML, tracing its evolution from early metalearning, hyperparameter optimization, and transfer learning techniques to the latest advancements in neural architecture search, automated pipeline design, and few-shot learning. It covers historical context, classical approaches, and modern applications while also addressing emerging topics. Key research directions are highlighted, focusing on enhancing model interpretability, improving generalization and robustness, expanding automated pipeline design, and ethical implications of AutoML technologies. This paper aims to provide a holistic view of the current state of AutoML, serving as a valuable resource for researchers, practitioners, and stakeholders seeking to understand and advance the capabilities of AutoML in both theoretical and practical contexts.

Intensity-modulated radiation therapy (IMRT) is an advanced technique for cancer treatment that uses a computer-controlled linear accelerator to customise beams’ radiation intensities for patients, optimising the treatment effectiveness. The complexity of IMRT planning requires sophisticated algorithms to solve the different optimisation problems that arise in the context of IMRT treatment planning. One of those optimisation problems is the Direct Aperture Optimisation (DAO). The DAO problem aims to find a set of aperture shapes for each beam angle to enhance precision and improve clinical outcomes. However, this process is computationally intensive and thus, heuristic approaches have been proposed to balance computational efficiency and solution quality, offering nearly optimal solutions within clinically acceptable times. This systematic literature review aims to trace the development and application of heuristic algorithms for the DAO problem in the context of IMRT over the past two decades. We synthesised 41 studies published between 2002 and 2023, sourced from seven major databases (ACM, IEEE Xplore, PubMed, ScienceDirect, Springer, Scopus, and Web of Science). The review highlights key trends, innovations, and future directions in using heuristic methods for DAO, providing valuable insights for researchers and practitioners in radiotherapy optimisation.

Image segmentation aims to partition an image into non-overlapping regions that are coherent in appearance. Although the fuzzy C-means (FCM) algorithm is widely used for its simplicity and efficiency, it treats each pixel independently and is therefore sensitive to noise. We propose LIFWCM, a local information-based fuzzy weighted C-means algorithm that assigns a single-pass, data-driven weight to each pixel by aggregating neighborhood intensity variation and positional overlap, and then integrates these weights into the standard FCM objective and a spatially aware membership refinement. This design suppresses the influence of noisy and boundary pixels while preserving details with low computational overhead. Across six experiments on synthetic images and natural images from the Image Processing Toolbox and BSDS500, LIFWCM consistently improves segmentation quality under heavy noise. On the BSDS500 image with 30% salt-and-pepper noise, LIFWCM attains 98.96% segmentation accuracy, exceeding the best baseline, and surpassing classical FCM variants. LIFWCM also achieves higher MPA (0.94) and MIoU (0.82) than competing methods, while converging in a few iterations. These results demonstrate that LIFWCM is robust to high-intensity noise, preserves fine structures, and remains efficient due to one-time weight computation, making it suitable for real-world noisy images with complex structures.

Many real-world machine learning classification problems suffer from imbalanced training data, where the least frequent label has high relevance and significance for the end user, such as equipment breakdowns or various types of process anomalies. This imbalance can negatively impact the learning algorithm and lead to misclassification of minority labels, resulting in erroneous actions and potentially high unexpected costs. Most previous oversampling methods rely only on the minority samples, often ignoring their overall density and distribution in relation to the other classes. In addition, most of them lack in the oversampling method’s explainability. In contrast, this paper proposes a novel oversampling method that considers a subspace of the feature-set for the creation of synthetic minority samples using nonlinear optimization of a class-sensitive objective function. Suitable subspaces for oversampling are identified through a Bayesian reinforcement strategy based on Dirichlet smoothing, which may be useful for explainable-AI. An empirical comparison of the proposed method is performed with 10 existing techniques on 18 real-world datasets using two traditional machine learning classifiers and four evaluation metrics. Statistical analysis of cross-validated runs over the 18 datasets and four metrics (i.e. 72 experiments) reveals that the proposed approach is among the best performing methods in 6 and 2 instances when using random forest classifier and support vector machine classifier, thus placing it at the top. The study also reveals that some feature combinations are more important than others for minority oversampling, and the proposed approach offers a way to identify such features.

Traditional variable importance measures quantify overall feature contributions but often overlook individual-level heterogeneity. Several new procedures attempt to address this limitation but remain model dependent and may introduce biases. We propose individual variable priority (iVarPro), an extension of the Variable Priority (VarPro) framework, which uses rule-based, data-driven partitioning to estimate the gradient of the conditional mean function. By focusing on gradients, iVarPro captures how small perturbations in a variable influence an individual’s outcome, providing a more precise and interpretable measure of importance. To demonstrate its advantages, we conducted simulations and analyzed a real-world survival dataset. Our results show that iVarPro more accurately captures the true functional relationship by flexibly leveraging local samples.

The cloud-edge-end collaboration system provides a new impetus for the development of intelligent transportation. In order to optimize the quality of service for intelligent transportation system users and improve system resource utilization, A three-tier caching strategy for cloud-edge-end collaboration based on efficiency collaboration task popularity (CSEPCA) was proposed, which exploits server resource characteristics and performs fine-grained cache replacements based on real-time task popularity to address the challenges associated with balancing server cache space and cost. To achieve an optimal balance between server cache space and cost, the problem of determining the availability of server cache space is formulated as a constrained markov decision process (CMDP), and an enhanced deep reinforcement learning algorithm based on soft updating (AT-SAC) was designed to achieve multi-objective optimization of system latency, energy consumption, and resource depletion rate, with the aim of improving service response speed and enhancing user service quality. To address challenges in effectively serving vehicles in areas with weak communication signals from cloud-edge servers, UAV swarms were introduced to assist with vehicle task offloading computations. A comprehensive optimization algorithm (Co-DRL-P) was proposed, which integrates enhanced deep reinforcement Learning (ERDDPG) and improved particle swarm optimization (A-PSO) algorithms to optimize UAV trajectories and communication angles, aiming to deliver superior service quality to users. Finally, we evaluate the performance of the proposed scheme through comprehensive simulation experiments. Specifically, when the number of users is 30, the system latency of the proposed scheme is 17.9%, 11.5%, 2.6%, and 60.2% lower than baseline schemes such as DQN, DDPG, TD3, and collaborative randomized schemes, and the system energy consumption is reduced by 20.6%, 15.9%, 9.4%, and 129.9%. Notably, the overall system cost for drone-assisted user offloading is reduced by approximately 49.6% in areas with weak cloud server signals.

This study introduces the Scientific Approach to Problem Solving-inspired Optimization (SAPSO) algorithm, a novel metaheuristic specifically designed for applications in civil engineering informatics. SAPSO imitates the structured process of scientific inquiry—covering problem review, hypothesis formulation, data collection, and analysis—to systematically explore complex search spaces. This approach enables SAPSO to reliably identify global optima. The algorithm’s performance was extensively tested against eleven leading metaheuristic algorithms using the IEEE Congress on Evolutionary Computation benchmark suites from 2020 (CEC 2020) and 2022 (CEC 2022). The comparison included the Artificial Bee Colony, Cultural Algorithm, Genetic Algorithm, Differential Evolution, Artificial Gorilla Troops Optimizer, Grey Wolf Optimizer, Particle Swarm Optimization, Red Kite Optimization Algorithm, Symbiotic Organisms Search, Teaching–Learning-Based Optimization, and Whale Optimization Algorithm. Statistical analysis with the Wilcoxon rank-sum test confirmed SAPSO’s superior results across these benchmarks. Additionally, this study presents a stacked ensemble machine learning framework called the SAPSO-Weighted Features Stacking System (SAPSO-WFSS), which combines SAPSO with two predictive models: a Radial Basis Function Neural Network and Least Squares Support Vector Regression. SAPSO is used to optimize both feature weights and model hyperparameters. Experiments on five diverse civil engineering case studies show that SAPSO-WFSS provides high accuracy, with Mean Absolute Percentage Error values as low as 2.4%, outperforming traditional methods. These findings demonstrate SAPSO’s potential as a powerful tool for improving prediction reliability in infrastructure maintenance and solving complex optimization problems in civil engineering.