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CoverLib: Classifiers-Equipped Experience Library by Iterative Problem Distribution Coverage Maximization for Domain-Tuned Motion Planning

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IEEE TRANSACTIONS ON ROBOTICS 2025年 41卷 2911-2930页

作者： Ishida, Hirokazu Hiraoka, Naoki Okada, Kei Inaba, Masayuki Univ Tokyo Tokyo 1138656 Japan

Library-based methods are known to be very effective for fast motion planning by adapting an experience retrieved from a precomputed library. This article presents CoverLib, a principled approach for constructing and utilizing such a library. CoverLib iteratively adds an experience-classifier-pair to the library, where each classifier corresponds to an adaptable region of the experience within the problem space. This iterative process is an active procedure, as it selects the next experience based on its ability to effectively cover the uncovered region. During the query phase, these classifiers are utilized to select an experience that is expected to be adaptable for a given problem. Experimental results demonstrate that CoverLib effectively mitigates the tradeoff between plannability and speed observed in global (e.g., sampling-based) and local (e.g., optimization-based) methods. As a result, it achieves both fast planning and high success rates over the problem domain. Moreover, due to its adaptation-algorithm-agnostic nature, CoverLib seamlessly integrates with various adaptation methods, including nonlinear programming-based and sampling-based algorithms.

关键词： Libraries Planning Trajectory Robots classification algorithms Training Dimensionality reduction Data mining Computational efficiency Sorting Data-driven planning motion and path planning sampling-based motion planning trajectory library trajectory optimization

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Enhancing Cardiovascular Disease Diagnosis: The Power of Optimized Ensemble Learning

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IEEE ACCESS 2025年 13卷 46747-46762页

作者： Yazdi, Fatemeh Asadi, Shahrokh Univ Tehran Coll Farabi Dept Engn Data Min Lab Tehran *** Iran

Cardiovascular disease, a leading cause of global mortality, is influenced by genetic, environmental, and lifestyle factors. Accurate diagnosis involves identifying key risk factors such as age, gender, family history, diabetes, obesity, and sedentary lifestyle, using medical history, physical exams, and lab tests. This study introduces an advanced method called GMM-based Dynamic Ensemble Learning Optimization using a Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D). This approach utilizes evolutionary resampling inspired by the Gaussian mixture model (GMM) to enhance classifier diversity, performance, and quantity, producing optimal datasets. Ensemble weight interpolation and dynamic ensemble techniques are applied to reduce overfitting. Tested on seven heart disease datasets with varying imbalance rates, the proposed method consistently surpasses established ensemble algorithms across multiple metrics, validated by McNemar's and Wilcoxon tests. Additionally, Kaplan Meier Estimates analysis of a heart failure dataset provides deeper insights into heart disease prognosis. This research advances accurate heart disease prediction and prognosis through innovative data mining and ensemble learning techniques.

关键词： Ensemble learning Diseases Medical diagnostic imaging Accuracy Heart Heuristic algorithms Optimization Cardiovascular diseases Overfitting classification algorithms evolutionary multi-objective optimization bootstrap diversity heart disease

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Advanced stacking models for machine fault diagnosis with ensemble trees and SVM

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APPLIED INTELLIGENCE 2025年第3期55卷 1-12页

作者： Liao, Yuhua Li, Ming Sun, Qingshuai Li, Pude Univ Manchester Dept Mech Aerosp & Civil Engn Manchester M13 9PL England Hunan Univ Sci & Technol Coll Informat & Elect Engn Xiangtan 411201 Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China

Fault diagnosis plays an integral role in machine health monitoring. However, in practical applications, there are obvious differences in class distribution within the data, leading to poor performance of the algorithm in identifying a few classes. Meanwhile, overfitting and computational resource requirements have become a challenge. Recently, the stacking model has been promoted in the field of fault diagnosis, but its performance evaluation of stacking models in many literature is not comprehensive enough. In this paper, an Advanced Ensemble Trees model (AET) is proposed. The SMOTE (Synthetic Minority Oversampling Technique) resampling technique is used to optimise the dataset balance. Then, the advantages of Support Vector Machines (SVM) and multi-tree models are combined to form a robust base model using hyper-parameter tuning. Simple Logistic Regression (LR) is used as a meta-model to construct the new stacking model. Through extensive experimental validation, it is found that the AET model is close to 99% in several key performance metrics and outperforms existing machine learning methods and relatively short model training time.

关键词： Intelligent fault diagnosis Machine learning classification algorithms Stacking models Imbalanced datasets

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R-CNN Based Vehicle Object Detection via Segmentation Capabilities in Road Scenes

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IEEE ACCESS 2025年 13卷 3355-3370页

作者： Riaz Chughtai, Bisma Alhasson, Haifa F. Alnusayri, Mohammed Alatiyyah, Mohammed Aljuaid, Hanan Jalal, Ahmad Park, Jeongmin Air Univ Fac Comp & AI Islamabad 44000 Pakistan Qassim Univ Coll Comp Dept Informat Technol Buraydah 52571 Saudi Arabia Jouf Univ Coll Comp & Informat Sci Dept Comp Sci Sakaka 72388 Saudi Arabia Prince Sattam bin Abdulaziz Univ Coll Comp Engn & Sci Dept Comp Sci Al Kharj 16278 Saudi Arabia Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Comp Sci Riyadh 11671 Saudi Arabia Korea Univ Coll Informat Dept Comp Sci & Engn Seoul 02841 South Korea Tech Univ Korea Dept Comp Engn Shihung 15073 Gyeonggi South Korea

In the realm of intelligent transportation systems, vehicle detection and classification stand as pivotal tasks. An effective traffic monitoring system should be capable of detecting, counting, and categorizing moving vehicles. Vehicle classification is a crucial task that can offer insights into road users and help make decisions to reduce congestion, for instance. This paper delves into advanced methodologies for detecting and classifying vehicles on roadways, addressing the limitations of traditional techniques that are often computationally intensive and data acquisition-sensitive. We propose a novel approach that leverages state-of-the-art machine learning and deep learning algorithms to enhance accuracy and efficiency. The proposed model comprises five stages. Initially, all images undergo preprocessing to reduce noise and enhance brightness. In the second stage, the foreground elements are extracted using segmentation techniques. The YOLOv8 algorithm is then used to process these segmented images to identify the vehicles inside them. Next, in the feature extraction phase, the detected vehicles are analyzed using Maximally Stable Estimated Features (MSER), Geometric features and Binary Robust Invariant Scalable Keypoints (BRISK) features. We employ the Recurrent Convolutional Neural Network (R-CNN) classifier for classification. Experimental results from two datasets demonstrate superior performance, with the model achieving an accuracy of 0.94% on the BITVehicle dataset and 0.98% on the Vehicle-OpenImage dataset. Additionally, a comparative analysis was conducted, showcasing the model's performance against the latest techniques in the field.

关键词： Feature extraction Accuracy Vehicle detection YOLO Convolutional neural networks Computational modeling Image segmentation classification algorithms Real-time systems Segmentation BRISK object detection vehicle recognition YOLOv8 recurrent convolutional neural network (RNN) machine learning deep learning deep learning

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DTSG-Net: Dynamic Time Series Graph Neural Network and Its Application in Modulation Recognition

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IEEE INTERNET OF THINGS JOURNAL 2025年第4期12卷 3742-3754页

作者： Yin, Peng Zhou, Jinchao Ge, Yizheng Chen, Zhuangzhi Univ Chinese Acad Sci Sch Cyber Secur Beijing 101408 Peoples R China Def Ind Secrecy Examinat & Certificat Ctr Beijing 100089 Peoples R China Binjiang Inst Artificial Intelligence Res Ctr Electromagnet Space Secur Hangzhou 310056 Peoples R China Def Ind Secrecy Examinat & Certificat Ctr Beijing 100089 Peoples R China

Modulation recognition of communication signals is of great importance in the context of the Internet of Everything (IoE), as wireless communication technology is a key foundation for implementing the IoE. Recently, graph neural networks (GNNs) have been successfully applied to modulation recognition tasks due to their ability to merge messages transmitted between adjacent nodes in the graph. However, GNN-based models are more computationally intensive when processing long signals, potentially reducing their practicality. In this article, we explore a novel signal representation from a graph perspective and propose a graph-powered modulation recognition framework. We first propose the dynamic time series graph (DTSG) algorithm, which segments the signals and maps each segment into a patch graph, with corresponding patches from different signals sharing connected edges. By integrating DTSG with both GNNs and recurrent neural networks (RNNs), we have designed an end-to-end signal classification framework, DTSG-Net, for modulation recognition. Experimental results on four datasets: 1) RML2016.10a;2) RML2018.01a;3) Sig2019-12;and 4) HKDD_AMC36-demonstrate that our DTSG-Net can achieve high signal modulation classification accuracy (Acc) with minimal computational resources, outperforming existing methods based on signal graph representation in terms of computational resource savings and higher accuracy.

关键词： Modulation Feature extraction Time series analysis Convolutional neural networks Heuristic algorithms Data mining Computational modeling Graph neural networks Deep learning classification algorithms Graph graph neural network (GNN) Internet of Everything (IoE) modulation recognition recurrent neural network (RNN)

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PMGDA: A Preference-Based Multiple Gradient Descent Algorithm

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年

作者： Zhang, Xiaoyuan Lin, Xi Zhang, Qingfu City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

It is desirable in many multi-objective machine learning applications, such as multi-task learning with conflicting objectives, multi-objective reinforcement learning, to find a Pareto solution that can match a given preference of a decision maker. These problems are often large-scale with available gradient information but cannot be handled very well by the existing algorithms. To tackle this issue, this paper proposes a novel predict-and-correct framework for locating a Pareto solution that fits the preference of a decision maker. In the proposed framework, a constraint function is introduced in the search progress to align the solution with a user-specific preference, which can be optimized simultaneously with multiple objective functions. Experimental results show that our proposed method can efficiently find a particular Pareto solution under the demand of a decision maker for standard multiobjective benchmark, multi-task learning, multi-objective reinforcement learning problems with more than thousands of decision variables.

关键词： Vectors Pareto optimization Optimization Multitasking Prediction algorithms Linear programming Machine learning algorithms Face recognition classification algorithms Artificial neural networks Multi-objective optimization multi-task learning multi-objective deep reinforcement learning preference-based optimization

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Through-Wall Multi-Person Action Recognition Using Enhanced YOLOv5 and IR-UWB Radar

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IEEE SENSORS JOURNAL 2025年第3期25卷 5711-5722页

作者： Li, Xiaoxiong Zhang, Shuning Chen, Si Xiao, Zelong Nanjing Univ Sci & Technol Sch Elect & Opt Engn Nanjing 210094 Peoples R China

Impulse radio ultrawideband (IR-UWB) radar has been extensively utilized in recognizing human activities due to its high resolution, independence from light and occlusion, and nonintrusive nature. However, current research is mainly limited to single-person and line-of-sight conditions, and many challenges exist in multi-person action recognition under non-line-of-sight (NLOS) conditions. This article proposes a through-wall, multi-person action recognition framework based on the improved YOLOv5 network. First, we collected the through-wall multi-person motion echoes using the IR-UWB radar. After processing them with algorithms such as clutter suppression and adaptive amplitude balancing, we successfully constructed a dataset containing time-range images of multi-person actions. Then, to improve the detection and classification performance, we improved the YOLOv5 network and introduced the bi-directional feature pyramid network (BiFPN) module, SCYLLA-intersection over union (SIoU) loss function, and coordinate attention (CA) mechanism. The BiFPN module enhances the model's learning ability for multiscale features. The SIoU loss function optimizes the positioning accuracy of the prediction box. The CA attention mechanism finely weights feature coordinates, enhancing the feature discrimination ability. The experimental results show that the improved YOLOv5 network achieves good detection and classification results on our dataset. Moreover, the proposed method has verified good generalization ability on the moving and stationary target acquisition and recognition (MSTAR) public dataset.

关键词： Radar YOLO Feature extraction Radar imaging Target recognition Radar detection Human activity recognition classification algorithms Accuracy Long short term memory Attention mechanism human activity recognition (HAR) multiple targets through-wall radar ultrawideband (UWB) radar

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A Systematic Review of Heart Sound Detection algorithms: Experimental Results and Insights

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Bi, Xiuli Tang, Shizhan Xiao, Bin Li, Weisheng Gao, Xinbo Lio, Pietro Chongqing Univ Posts & Telecommun Dept Comp Sci & Technol Chongqing 400065 Peoples R China Jinan Inspur Data Technol Co Ltd Jinan 250101 Peoples R China Univ Cambridge Dept Comp Sci & Technol Cambridge CB3 0FD England

Automatic heart sound detection plays a vital role in the early detection of cardiovascular diseases (CVDs). In recent years, many heart sound detection algorithms have been proposed, becoming a popular research topic in medical diagnosis. In this article, our goal is to answer which heart sound detection algorithms and heart sound features perform best in which situations and to explore the problems in existing research. This work will provide ideas for subsequent research in this area. We achieve this aim through a standard process that includes preprocessing, segmentation, feature extraction, and classification. We discuss the length and overlap rate in segmentation and analyze the classification methods, especially the nine most salient heart sound features. The performance of the existing techniques is evaluated in different datasets and at the same level of comparison. The experiments show that the segmentation length includes at least one complete period, and we should use a large overlap rate in the segmentation phase. For feature extraction, time-domain feature (TIME) and fast Fourier transform (FFT) features based on single independent variables perform better in deep models and traditional classifiers. Short-time Fourier transform (STFT), continuous wavelet transform (CWT), and S-transform (ST) features based on double independent variables can perform well in all classification models;Mel spectrum/Mel frequency cepstrum coefficient (MFCC) is only better on deep neural networks (DNNs). The best performance is achieved by combining TIME or MFCC with DNNs.

关键词： Heart Feature extraction Standards Phonocardiography Electrocardiography Detection algorithms Noise classification algorithms Training Time-domain analysis classification model comparative study feature extraction heart sound detection

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Systematic Review of Fake News, Propaganda, and Disinformation: Examining Authors, Content, and Social Impact Through Machine Learning

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IEEE ACCESS 2025年 13卷 17583-17629页

作者： Plikynas, Darius Rizgeliene, Ieva Korvel, Grazina Vilnius Univ Fac Math & Informat Inst Data Sci & Digital Technol Vilnius 10257 Lithuania

In recent years, the world has witnessed a global outbreak of fake news, propaganda and disinformation (FNPD) flows on online social networks (OSN). In the context of information warfare and the capabilities of generative AI, FNPDs have proliferated. They have become a powerful and quite effective tool for influencing people's social identities, attitudes, opinions and even behavior. Ad hoc malicious social media accounts and organized networks of trolls and bots target countries, societies, social groups, political campaigns and individuals. As a result, conspiracy theories, echo chambers, filter bubbles and other processes of fragmentation and marginalization are polarizing, radicalizing, and disintegrating society in terms of coherent politics, governance, and social networks of trust and cooperation. This systematic review aims to explore advances in using machine and deep learning to detect FNPD in OSNs effectively. We present the results of a combined PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) review in three analysis domains: 1) propagators (authors, trolls, and bots), 2) textual content, 3) social impact. This systemic research framework integrates meta-analyses of three research domains, providing an overview of the wider research field and revealing important relationships between these research domains. It not only addresses the most promising ML/DL research methodologies and hybrid approaches in each domain, but also provides perspectives and insights on future research directions.

关键词： Fake news Social networking (online) Systematic literature review Feature extraction Content management classification algorithms Analytical models Accuracy Security Machine learning deep learning propaganda and disinformation fake news PRISMA systematic review authors' analysis content analysis social impact analysis social impact analysis

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FLARE: Fuzzy Local Agnostic Rule-Based Explanations for Closed Box Classifiers

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年

作者： Fernandez, Guillermo Aledo, Juan A. Gamez, Jose A. Puerta, Jose M. Univ Castilla La Mancha Intelligent Syst & Data Min Lab SIMD Albacete 02071 Spain

The massive amount of data available in recent years has led to explosive growth in the field of machine learning. Closed box models gain from this because they can be trained more effectively and provide better results than Glass box models, albeit at the cost of interpretability. Critical applications such as medicine or aviation would benefit greatly from these more powerful models, but they cannot afford to trust a closed box model unquestioningly;hence, they need a way to explain these methods (or at least their reasoning when classifying an instance). A well-accepted practice is to build a surrogate explainable (Glass box) model that represents a small region around the instance to be explained by extracting factual and counterfactual explanations. A factual explanation justifies the classification of the given instance into a specific category, whereas a counterfactual explanation points out why that particular instance has not been classified differently. This work introduces FLARE, an agnostic algorithm that employs fuzzy logic to generate factual and counterfactual explanations. First, it generates a synthetic neighborhood around the instance. Then, it learns a fuzzy decision tree using the neighborhood. After that, factual and counterfactual explanations are extracted from the tree. Finally, to maintain the semantics of the problem domain, the explanations are mapped to the global fuzzy sets, defined over the entire range of each variable. Experiments have been conducted using well-known closed box algorithms, datasets, and state-of-the-art counterfactual methods. The results show that FLARE performs as well as the current best methods while being computed orders of magnitude faster.

关键词： Closed box Decision trees Fuzzy sets Linguistics Computational modeling Machine learning algorithms Glass box Cognition classification algorithms Prototypes Explainable artificial intelligence fuzzy decision trees counterfactuals machine learning fuzzy rule-based systems

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