Expert Systems最新文献_第2页

Label distribution learning for compound facial expression recognition in‐the‐wild: A comparative study 用于野外复合面部表情识别的标签分布学习：比较研究

IF 3.3 4区计算机科学

Expert Systems Pub Date : 2024-09-10 DOI: 10.1111/exsy.13724

Afifa Khelifa, Haythem Ghazouani, Walid Barhoumi

{"title":"Label distribution learning for compound facial expression recognition in‐the‐wild: A comparative study","authors":"Afifa Khelifa, Haythem Ghazouani, Walid Barhoumi","doi":"10.1111/exsy.13724","DOIUrl":"https://doi.org/10.1111/exsy.13724","url":null,"abstract":"Human emotional states encompass both basic and compound facial expressions. However, current works primarily focus on basic expressions, consequently neglecting the broad spectrum of human emotions encountered in practical scenarios. Compound facial expressions involve the simultaneous manifestation of multiple emotions on an individual's face. This phenomenon reflects the complexity and richness of human states, where facial features dynamically convey a combination of feelings. This study embarks on a pioneering exploration of Compound Facial Expression Recognition (CFER), with a distinctive emphasis on leveraging the Label Distribution Learning (LDL) paradigm. This strategic application of LDL aims to address the ambiguity and complexity inherent in compound expressions, marking a significant departure from the dominant Single Label Learning (SLL) and Multi‐Label Learning (MLL) paradigms. Within this framework, we rigorously investigate the potential of LDL for a critical challenge in Facial Expression Recognition (FER): recognizing compound facial expressions in uncontrolled environments. We utilize the recently introduced RAF‐CE dataset, meticulously designed for compound expression assessment. By conducting a comprehensive comparative analysis pitting LDL against conventional SLL and MLL approaches on RAF‐CE, we aim to definitively establish LDL's superiority in handling this complex task. Furthermore, we assess the generalizability of LDL models trained on RAF‐CE by evaluating their performance on the EmotioNet and RAF‐DB Compound datasets. This demonstrates their effectiveness without domain adaptation. To solidify these findings, we conduct a comprehensive comparative analysis of 12 cutting‐edge LDL algorithms on RAF‐CE, S‐BU3DFE, and S‐JAFFE datasets, providing valuable insights into the most effective LDL techniques for FER in‐the‐wild.","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"65 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Federated learning‐driven dual blockchain for data sharing and reputation management in Internet of medical things 用于医疗物联网数据共享和声誉管理的联邦学习驱动双区块链

IF 3.3 4区计算机科学

Expert Systems Pub Date : 2024-09-06 DOI: 10.1111/exsy.13714

Chenquan Gan, Xinghai Xiao, Qingyi Zhu, Deepak Kumar Jain, Akanksha Saini, Amir Hussain

{"title":"Federated learning‐driven dual blockchain for data sharing and reputation management in Internet of medical things","authors":"Chenquan Gan, Xinghai Xiao, Qingyi Zhu, Deepak Kumar Jain, Akanksha Saini, Amir Hussain","doi":"10.1111/exsy.13714","DOIUrl":"https://doi.org/10.1111/exsy.13714","url":null,"abstract":"In the Internet of Medical Things (IoMT), the vulnerability of federated learning (FL) to single points of failure, low‐quality nodes, and poisoning attacks necessitates innovative solutions. This article introduces a FL‐driven dual‐blockchain approach to address these challenges and improve data sharing and reputation management. Our approach comprises two blockchains: the Model Quality Blockchain (MQchain) and the Reputation Incentive Blockchain (RIchain). MQchain utilizes an enhanced Proof of Quality (PoQ) consensus algorithm to exclude low‐quality nodes from participating in aggregation, effectively mitigating single points of failure and poisoning attacks by leveraging node reputation and quality thresholds. In parallel, RIchain incorporates a reputation evaluation, incentive mechanism, and index query mechanism, allowing for rapid and comprehensive node evaluation, thus identifying high‐reputation nodes for MQchain. Security analysis confirms the theoretical soundness of the proposed method. Experimental evaluation using real medical datasets, specifically MedMNIST, demonstrates the remarkable resilience of our approach against attacks compared to three alternative methods.","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"6 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

One-step multiple kernel k-means clustering based on block diagonal representation 基于块对角线表示的一步多核 K 均值聚类法

IF 3 4区计算机科学

Expert Systems Pub Date : 2024-09-05 DOI: 10.1111/exsy.13720

Cuiling Chen, Zhi Li

{"title":"One-step multiple kernel k-means clustering based on block diagonal representation","authors":"Cuiling Chen, Zhi Li","doi":"10.1111/exsy.13720","DOIUrl":"10.1111/exsy.13720","url":null,"abstract":"Multiple kernel k-means clustering (MKKC) can efficiently incorporate multiple base kernels to generate an optimal kernel. Many existing MKKC methods all need two-step operation: learning clustering indicator matrix and performing clustering on it. However, the optimal clustering results of two steps are not equivalent to those of original problem. To address this issue, in this paper we propose a novel method named one-step multiple kernel k-means clustering based on block diagonal representation (OS-MKKC-BD). By imposing a block diagonal constraint on the product of indicator matrix and its transpose, this method can encourage the indicator matrix to be block diagonal. Then the indicator matrix can produce explicit clustering indicator, so as to implement one-step clustering, which avoids the disadvantage of two-step operation. Furthermore, a simple kernel weighting strategy is used to obtain an optimal kernel, which boosts the quality of optimal kernel. In addition, a three-step iterative algorithm is designed to solve the corresponding optimization problem, where the Riemann conjugate gradient iterative method is used to solve the optimization problem of the indicator matrix. Finally, by extensive experiments on eleven real data sets and comparison of clustering results with 10 MKC methods, it is concluded that OS-MKKC-BD is effective.","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"41 12","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A new method based on generative adversarial networks for multivariate time series prediction 基于生成式对抗网络的多变量时间序列预测新方法

IF 3 4区计算机科学

Expert Systems Pub Date : 2024-09-05 DOI: 10.1111/exsy.13700

Xiwen Qin, Hongyu Shi, Xiaogang Dong, Siqi Zhang

{"title":"A new method based on generative adversarial networks for multivariate time series prediction","authors":"Xiwen Qin, Hongyu Shi, Xiaogang Dong, Siqi Zhang","doi":"10.1111/exsy.13700","DOIUrl":"10.1111/exsy.13700","url":null,"abstract":"Multivariate time series have more complex and high-dimensional characteristics, which makes it difficult to analyze and predict the data accurately. In this paper, a new multivariate time series prediction method is proposed. This method is a generative adversarial networks (GAN) method based on Fourier transform and bi-directional gated recurrent unit (Bi-GRU). First, the Fourier transform is utilized to extend the data features, which helps the GAN to better learn the distributional features of the original data. Second, in order to guide the model to fully learn the distribution of the original time series data, Bi-GRU is introduced as the generator of GAN. To solve the problems of mode collapse and gradient vanishing that exist in GAN, Wasserstein distance is used as the loss function of GAN. Finally, the proposed method is used for the prediction of air quality, stock price and RMB exchange rate. The experimental results show that the model can effectively predict the trend of the time series compared with the other nine baseline models. It significantly improves the accuracy and flexibility of multivariate time series forecasting and provides new ideas and methods for accurate time series forecasting in industrial, financial and environmental fields.","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"41 12","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142226348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TensorCRO: A TensorFlow-based implementation of a multi-method ensemble for optimization TensorCRO：基于 TensorFlow 的多方法优化组合实施方案

IF 3 4区计算机科学

Expert Systems Pub Date : 2024-09-05 DOI: 10.1111/exsy.13713

A. Palomo-Alonso, V. G. Costa, L. M. Moreno-Saavedra, E. Lorente-Ramos, J. Pérez-Aracil, C. E. Pedreira, S. Salcedo-Sanz

{"title":"TensorCRO: A TensorFlow-based implementation of a multi-method ensemble for optimization","authors":"A. Palomo-Alonso, V. G. Costa, L. M. Moreno-Saavedra, E. Lorente-Ramos, J. Pérez-Aracil, C. E. Pedreira, S. Salcedo-Sanz","doi":"10.1111/exsy.13713","DOIUrl":"10.1111/exsy.13713","url":null,"abstract":"This paper presents a novel implementation of the Coral Reef Optimization with Substrate Layers (CRO-SL) algorithm. Our approach, which we call TensorCRO, takes advantage of the TensorFlow framework to represent CRO-SL as a series of tensor operations, allowing it to run on GPU and search for solutions in a faster and more efficient way. We evaluate the performance of the proposed implementation across a wide range of benchmark functions commonly used in optimization research (such as the Rastrigin, Rosenbrock, Ackley, and Griewank functions), and we show that GPU execution leads to considerable speedups when compared to its CPU counterpart. Then, when comparing TensorCRO to other state-of-the-art optimization algorithms (such as the Genetic Algorithm, Simulated Annealing, and Particle Swarm Optimization), the results show that TensorCRO can achieve better convergence rates and solutions than other algorithms within a fixed execution time, given that the fitness functions are also implemented on TensorFlow. Furthermore, we also evaluate the proposed approach in a real-world problem of optimizing power production in wind farms by selecting the locations of turbines; in every evaluated scenario, TensorCRO outperformed the other meta-heuristics and achieved solutions close to the best known in the literature. Overall, our implementation of the CRO-SL algorithm in TensorFlow GPU provides a new, fast, and efficient approach to solving optimization problems, and we believe that the proposed implementation has significant potential to be applied in various domains, such as engineering, finance, and machine learning, where optimization is often used to solve complex problems. Furthermore, we propose that this implementation can be used to optimize models that cannot propagate an error gradient, which is an excellent choice for non-gradient-based optimizers.","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"41 12","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.13713","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparative evaluation of Large Language Models using key metrics and emerging tools 使用关键指标和新兴工具对大型语言模型进行比较评估

IF 3.3 4区计算机科学

Expert Systems Pub Date : 2024-09-04 DOI: 10.1111/exsy.13719

Sarah McAvinue, Kapal Dev

{"title":"Comparative evaluation of Large Language Models using key metrics and emerging tools","authors":"Sarah McAvinue, Kapal Dev","doi":"10.1111/exsy.13719","DOIUrl":"https://doi.org/10.1111/exsy.13719","url":null,"abstract":"This research involved designing and building an interactive generative AI application to conduct a comparative analysis of two advanced Large Language Models (LLMs), GPT‐4, and Claude 2, using Langsmith evaluation tools. The project was developed to explore the potential of LLMs in facilitating postgraduate course recommendations within a simulated environment at Munster Technological University (MTU). Designed for comparative analysis, the application enables testing of GPT‐4 and Claude 2 and can be hosted flexibly on either Amazon Web Services (AWS) or Azure. It utilizes advanced natural language processing and retrieval‐augmented generation (RAG) techniques to process proprietary data tailored to postgraduate needs. A key component of this research was the rigorous assessment of the LLMs using the Langsmith evaluation tool against both customized and standard benchmarks. The evaluation focused on metrics such as bias, safety, accuracy, cost, robustness, and latency. Additionally, adaptability covering critical features like language translation and internet access, was independently researched since the Langsmith tool does not evaluate this metric. This ensures a holistic assessment of the LLM's capabilities.","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"15 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ACRES: A framework for (semi)automatic generation of rule-based expert systems with uncertainty from datasets ACRES：从数据集（半）自动生成具有不确定性的基于规则的专家系统的框架

IF 3 4区计算机科学

Expert Systems Pub Date : 2024-09-04 DOI: 10.1111/exsy.13723

Konstantinos Kovas, Ioannis Hatzilygeroudis

{"title":"ACRES: A framework for (semi)automatic generation of rule-based expert systems with uncertainty from datasets","authors":"Konstantinos Kovas, Ioannis Hatzilygeroudis","doi":"10.1111/exsy.13723","DOIUrl":"10.1111/exsy.13723","url":null,"abstract":"Traditionally, the design of an expert system involves acquiring knowledge, in the form of symbolic rules, directly from the expert(s), which is a complex and time-consuming task. Although expert systems approach is quite old, it is still present, especially where explicit knowledge representation and reasoning, which assure interpretability and explainability, are necessary. Therefore, machine learning methods have been devised to extract rules from data, to facilitate that task. However, those methods are quite inflexible in adapting to the application domain and provide no help in designing the expert system. In this work, we present a framework and corresponding tool, namely ACRES, for semi-automatically generating expert systems from datasets. ACRES allows for data preprocessing, which helps in structuring knowledge in the form of a tree, called rule hierarchy, which represents (possible) dependencies among data variables and is used for rule formation. This improves interpretability and explainability of the produced systems. We have also designed and evaluated alternative methods for rule extraction from data and for calculation and use of certainty factors, to represent uncertainty; CFs can be dynamically updated. Experimental results on seven well-known datasets show that the proposed rule extraction methods are comparable to other popular machine learning approaches like decision trees, CART, JRip, PART, Random Forest, and so on, for the classification task. Finally, we give insights on two applications of ACRES.","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"41 12","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.13723","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

How does energy transition improve energy utilization efficiency? A case study of China's coal‐to‐gas program 能源转型如何提高能源利用效率？中国煤制天然气项目案例研究

IF 3.3 4区计算机科学

Expert Systems Pub Date : 2024-09-03 DOI: 10.1111/exsy.13721

Zhixiang Zhou, Yifei Zhu, Yannan Li, Huaqing Wu

引用次数: 0

Deep learning-based gesture recognition for surgical applications: A data augmentation approach 基于深度学习的手术应用手势识别：数据增强方法

IF 3 4区计算机科学

Expert Systems Pub Date : 2024-09-01 DOI: 10.1111/exsy.13706

Sofía Sorbet Santiago, Jenny Alexandra Cifuentes

{"title":"Deep learning-based gesture recognition for surgical applications: A data augmentation approach","authors":"Sofía Sorbet Santiago, Jenny Alexandra Cifuentes","doi":"10.1111/exsy.13706","DOIUrl":"10.1111/exsy.13706","url":null,"abstract":"Hand gesture recognition and classification play a pivotal role in automating Human-Computer Interaction (HCI) and have garnered substantial attention in research. In this study, the focus is placed on the application of gesture recognition in surgical settings to provide valuable feedback during medical training. A tool gesture classification system based on Deep Learning (DL) techniques is proposed, specifically employing a Long Short Term Memory (LSTM)-based model with an attention mechanism. The research is structured in three key stages: data pre-processing to eliminate outliers and smooth trajectories, addressing noise from surgical instrument data acquisition; data augmentation to overcome data scarcity by generating new trajectories through controlled spatial transformations; and the implementation and evaluation of the DL-based classification strategy. The dataset used includes recordings from ten participants with varying surgical experience, covering three types of trajectories and involving both right and left arms. The proposed classifier, combined with the data augmentation strategy, is assessed for its effectiveness in classifying all acquired gestures. The performance of the proposed model is evaluated against other DL-based methodologies commonly employed in surgical gesture classification. The results indicate that the proposed approach outperforms these benchmark methods, achieving higher classification accuracy and robustness in distinguishing diverse surgical gestures.","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"41 12","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CADICA: A new dataset for coronary artery disease detection by using invasive coronary angiography CADICA：利用有创冠状动脉造影检测冠状动脉疾病的新数据集

IF 3 4区计算机科学

Expert Systems Pub Date : 2024-08-29 DOI: 10.1111/exsy.13708

Ariadna Jiménez-Partinen, Miguel A. Molina-Cabello, Karl Thurnhofer-Hemsi, Esteban J. Palomo, Jorge Rodríguez-Capitán, Ana I. Molina-Ramos, Manuel Jiménez-Navarro

{"title":"CADICA: A new dataset for coronary artery disease detection by using invasive coronary angiography","authors":"Ariadna Jiménez-Partinen, Miguel A. Molina-Cabello, Karl Thurnhofer-Hemsi, Esteban J. Palomo, Jorge Rodríguez-Capitán, Ana I. Molina-Ramos, Manuel Jiménez-Navarro","doi":"10.1111/exsy.13708","DOIUrl":"10.1111/exsy.13708","url":null,"abstract":"Coronary artery disease (CAD) remains the leading cause of death globally and invasive coronary angiography (ICA) is considered the gold standard of anatomical imaging evaluation when CAD is suspected. However, risk evaluation based on ICA has several limitations, such as visual assessment of stenosis severity, which has significant interobserver variability. This motivates to development of a lesion classification system that can support specialists in their clinical procedures. Although deep learning classification methods are well-developed in other areas of medical imaging, ICA image classification is still at an early stage. One of the most important reasons is the lack of available and high-quality open-access datasets. In this paper, we reported a new annotated ICA images dataset, CADICA, to provide the research community with a comprehensive and rigorous dataset of coronary angiography consisting of a set of acquired patient videos and associated disease-related metadata. This dataset can be used by clinicians to train their skills in angiographic assessment of CAD severity, by computer scientists to create computer-aided diagnostic systems to help in such assessment, and to validate existing methods for CAD detection. In addition, baseline classification methods are proposed and analysed, validating the functionality of CADICA with deep learning-based methods and giving the scientific community a starting point to improve CAD detection.","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"41 12","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.13708","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142226349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0