Expert Systems with Applications最新文献

{"title":"Anticipating impression using textual sentiment based on ensemble LRD model","authors":"Abdul Karim ,&nbsp;Maria Mansab ,&nbsp;Mobeen Shahroz ,&nbsp;Muhammad Faheem Mushtaq ,&nbsp;In cheol Jeong","doi":"10.1016/j.eswa.2024.125717","DOIUrl":"10.1016/j.eswa.2024.125717","url":null,"abstract":"<div><div>Twitter sentiment analysis is a natural language processing that analyzes the sentiments espoused in Twitter tweets, helping users understand others’ perspectives on specific issues or trends. The research aims to improve sentiment analysis applications across industries by optimizing machine learning models for accurate sentiment prediction in diverse textual data. The goal of this study is to make the development of strong ensemble learning models by utilizing a publicly available dataset, such as Twitter sentiment analysis through Kaggle. To carefully clean the data and remove any unnecessary information, preprocessing techniques are used. The data is divided into two sections to predict impressions: training data and testing data, and seven different machine learning methods are applied such as Naive Bayes Classifiers, Logistic Regression, Decision Trees, Support Vector Machines, Multilayer Perceptron, Gradient Boosting, three classifiers that were merged into one ensemble machine learning approach. To determine each words weight value within the text of a document, the TF-IDF technique is applied. The trained model is compared to testing data to determine how much variance exists between actual and expected values. The result is evaluated using evaluation parameters such as precision, recall, and F1 score. The maximum accuracy achieved by the ensemble LRD model is approximately 90.5 %. This study aims to enhance sentiment analysis in various industries and sentiment-based recommendation systems, by analyzing diverse texts and determining people’s perspectives.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"263 ","pages":"Article 125717"},"PeriodicalIF":7.5,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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}

{"title":"Trusted commonsense knowledge enhanced depression detection based on three-way decision","authors":"Jie Chen,&nbsp;Hui Yao,&nbsp;Shu Zhao,&nbsp;Yanping Zhang","doi":"10.1016/j.eswa.2024.125671","DOIUrl":"10.1016/j.eswa.2024.125671","url":null,"abstract":"<div><div>Depression detection on social media aims to identify depressive tendencies within textual posts, providing timely intervention by the early detection of mental health issues. In predominant approaches, the Pre-trained Language Models(PLMs) are trained solely on public datasets, falling short of vertical scenarios due to insufficient domain-specific and commonsense knowledge. In addition, ambiguous commonsense knowledge could be misleading to PLMs and results in false judgments. Therefore, it poses significant challenges to select commonsense knowledge that is trusted. To address this, we propose CoKE, a model that incorporates trusted commonsense knowledge based on three-way decision theory to enhance depression detection. CoKE comprises three key modules: trusted screening, knowledge generation, and knowledge fusion. First, we utilize psychiatric clinical scales and three-way decision theory to screen out the uncertain domain from the massive user posts. Then, an adaptive framework is applied to generate and refine trusted commonsense knowledge that can explain the true semantics of posts in the uncertain domain. Finally, a dynamic integration of posts with highly trusted knowledge is achieved through a gating mechanism, resulting in embeddings enhanced by trusted commonsense knowledge that are more effective in determining depressive tendencies. We evaluate our model on two prominent datasets, eRisk2017 and eRisk2018, demonstrating its superiority over previous state-of-the-art baseline models.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"263 ","pages":"Article 125671"},"PeriodicalIF":7.5,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MSU-Net: Multi-Scale self-attention semantic segmentation method for oil-tea camellia planting area extraction in hilly areas of southern China","authors":"Zikun Xu ,&nbsp;Hengkai Li ,&nbsp;Beiping Long","doi":"10.1016/j.eswa.2024.125779","DOIUrl":"10.1016/j.eswa.2024.125779","url":null,"abstract":"<div><div>Oil-tea camellia, one of the world’s four major edible woody oil trees, is acclaimed as the ’Oriental Olive Oil’ due to its exceptionally high nutritional value. The climate in southern China synchronizes with the ideal conditions for cultivating oil tea, making it the most abundant region globally in terms of its distribution. Consequently, the delineation of oil tea cultivation zones holds paramount significance for agricultural authorities in devising strategic production plans and management. However, the region is often affected by changeable weather and frequent cloud and rain, and there is a lack of continuous optical image data. Moreover, the complex topography primarily characterized by mountainous terrain, extensive coverage of farmlands, and vegetation has fragmented topographic features, posing challenges in accurately extracting semantic information from remote sensing images. To address these challenges, we propose a multi-scale self-attention semantic segmentation network aimed at meticulously identifying the semantic features of oil tea. Specifically, we introduce a self-attention mechanism to enable the model to comprehensively understand the information on feature images, followed by the integration of multi-scale feature images through the ASPP(Atrous Spatial Pyramid Pooloing) module to prevent the oversight of minor terrain features. Finally, the Dice-Loss function is applied to optimize the model’s segmentation of edge details. Experimental evaluations demonstrate that the proposed multi-scale self-attention semantic segmentation model achieved an Intersection over Union (IOU) of 0.93, Pixel Accuracy (PA) of 0.98, and Overall Accuracy (OA) of 94.83% for oil tea extraction on the dataset, showcasing a notable improvement over the original model. Additionally, we explore the method’s data requirements from the perspective of data volume and proportion. Ultimately, the experimental results demonstrate that our proposed method can accurately extract the oil tea cultivation areas in the cloudy and rainy hilly regions of southern China with high precision, thereby serving as a technological means for agricultural departments to oversee oil tea cultivation.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"263 ","pages":"Article 125779"},"PeriodicalIF":7.5,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DAN: Neural network based on dual attention for anomaly detection in ICS","authors":"Lijuan Xu ,&nbsp;Bailing Wang ,&nbsp;Dawei Zhao ,&nbsp;Xiaoming Wu","doi":"10.1016/j.eswa.2024.125766","DOIUrl":"10.1016/j.eswa.2024.125766","url":null,"abstract":"<div><div>In the interpretability research on anomalies of Industrial Control Systems (ICS) with Graph Convolutional Neural Networks (GCN), the causality between the equipment components is a non-negligible factor. Nonetheless, few existing interpretable anomaly detection methods keeps a good balance of detection and interpretation, because of inadequate insufficient learning of causality and improper representation of nodes in GCN. In this paper, we propose a Dual Attention Network (DAN) for a multivariate time series anomaly detection approach, in which temporal causality based on attention is used for representing the relationship of device components. With this condition, the performance of detection is hardly satisfactory. In addition, in the existing graph neural networks, hyperparameters are used to construct an adjacency matrix, so that the detection accuracy is greatly affected. To address the above problems, we introduce a graph neural network based on an attention mechanism to further learn the causal relationship between device components, and propose an adjacency matrix construction method based on the median, to break through the constraint of hyperparameters. In terms of interpretation and detection effect, the performed experiments using the SWaT and WADI datasets from highly simulated real water plants, demonstrate the validity and universality of the DAN.<span><span>¹</span></span></div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"263 ","pages":"Article 125766"},"PeriodicalIF":7.5,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A reinforcement learning-enhanced multi-objective iterated greedy algorithm for weeding-robot operation scheduling problems","authors":"Zhonghua Miao,&nbsp;Hengwei Guo,&nbsp;Quan-ke Pan,&nbsp;Chen Peng,&nbsp;Ziyu Xu","doi":"10.1016/j.eswa.2024.125760","DOIUrl":"10.1016/j.eswa.2024.125760","url":null,"abstract":"<div><div>With technological advancements, robots have been widely used in various fields and play a vital role in the production execution system of a smart farm. However, the operation scheduling problem of robots within production execution systems has not received much attention so far. To enable efficient management, this paper develops a multi-objective mathematical model concerning both the efficiency and economic indicators. We propose a population-based iterated greedy algorithm enhanced with Q-learning (Q_DPIG) for a multi-weeding-robots operation scheduling problem. An index-based heuristic (IBH) is designed to generate a diverse set of initial solutions, while an adaptive destruction phase, guided by the Q-learning framework, ensures effective neighborhood search and solution optimization. Additionally, a local search method focusing on the high-load and the critical robots is employed to further optimize the two objectives. Finally, Q_DPIG is demonstrated to be effective and significantly outperform the state-of-the-art algorithms through comprehensive test datasets and a real case study from a farmland management center.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"263 ","pages":"Article 125760"},"PeriodicalIF":7.5,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Scheduling identical parallel machines involving flexible maintenance activities","authors":"Chunhao Li ,&nbsp;Feng Wang ,&nbsp;Jatinder N.D. Gupta ,&nbsp;Tsui-Ping Chung","doi":"10.1016/j.eswa.2024.125722","DOIUrl":"10.1016/j.eswa.2024.125722","url":null,"abstract":"<div><div>Motivated by a practical situation in chip manufacturing process, for the first time in the literature, this paper considers an identical parallel-machine scheduling problem with new flexible maintenance activities to minimize makespan where a maintenance activity is needed if and only if the machine capability has deteriorated by a critical value. To address the proposed problem, a mixed integer linear programming model and a lower bound are established. Since this problem is NP-hard, a combined constructive heuristic algorithm with six priority rules is developed. In order to improve the solution obtained by the proposed combined heuristic algorithm, an embedded learning mechanism is combined with the existing artificial immune system (AIS) algorithm to help self-adjust and modify the search direction. The effectiveness of the proposed combined constructive heuristic and the AIS algorithms is empirically tested on the randomly generated problem instances. These computational results show that the proposed AIS algorithm can generate better near-optimal solutions than several adaptations of the existing algorithms.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"263 ","pages":"Article 125722"},"PeriodicalIF":7.5,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction and reliability analysis of ultimate axial strength for outer circular CFRP-strengthened CFST columns with CTGAN and hybrid MFO-ET model","authors":"Viet-Linh Tran ,&nbsp;Jaehong Lee ,&nbsp;Jin-Kook Kim","doi":"10.1016/j.eswa.2024.125704","DOIUrl":"10.1016/j.eswa.2024.125704","url":null,"abstract":"<div><div>This study develops a novel hybrid machine learning model to estimate the ultimate axial strength and conduct a reliability analysis for outer circular carbon fiber-reinforced polymer (CFRP)-strengthened concrete-filled steel tube (CFST) columns. The experimental datasets are collected and enriched using the conditional tabular generative adversarial network (CTGAN). The column length, the steel properties (cross-section diameter, thickness, and yield strength), the CFRP properties (thickness, tensile strength, and elastic modulus), and concrete strength are selected as input variables to develop the Extra Trees (ET) model hybridized with Moth-Flame Optimization (MFO) algorithm for the ultimate axial strength estimation. The results reveal that the CTGAN can efficiently capture the actual data distribution of CFRP-strengthened CFST columns and the developed hybrid MFO-ET model can accurately predict the ultimate axial strength with a high accuracy (R² of 0.985, A10 of 0.867, RMSE of 182.810 kN, and MAE of 124.534 kN) based on the synthetic database. In addition, compared with the best empirical model, the MFO-ET model increases the R² by (6.78% and 13.48%) and A10 by (108.19% and 122.88%) and reduces the RMSE by (68.19% and 66.24%) and MAE by (71.33% and 68.48%) based on real and synthetic databases, respectively. Notably, a reliability analysis is performed to evaluate the safety of the developed MFO-ET model using Monte Carlo Simulation (MCS). Finally, a web application tool is created to make the developed MFO-ET model easier for users to design practical applications.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"263 ","pages":"Article 125704"},"PeriodicalIF":7.5,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Segment Anything Model for fetal head-pubic symphysis segmentation in intrapartum ultrasound image analysis","authors":"Zihao Zhou ,&nbsp;Yaosheng Lu ,&nbsp;Jieyun Bai ,&nbsp;Víctor M. Campello ,&nbsp;Fan Feng ,&nbsp;Karim Lekadir","doi":"10.1016/j.eswa.2024.125699","DOIUrl":"10.1016/j.eswa.2024.125699","url":null,"abstract":"<div><div>The Angle of Progression (AoP), determined by the contour delineations of the pubic symphysis and fetal head (PSFH) in intrapartum ultrasound (US) images, is crucial for predicting delivery mode and significantly influences labor outcomes. However, automating AoP measurement based on PSFH segmentation remains challenging due to poor foreground-background contrast, blurred boundaries, and anatomical variability in shapes, sizes, and positions during labor. We propose a novel Segment Anything Model (SAM) framework, AoP-SAM, designed to enhance the PSFH segmentation, AoP measurement and outcome prediction, tackling the challenges of small target segmentation and accurate boundary segmentation. It synergistically combines CNNs and Transformers within a cooperative encoder to process complex spatial relationships and contextual information to segment the PSFH. In this encoder, we devise a multi-scale CNN branch to capture intrinsic local details, which compensates for the defects of the Transformer branch in extracting local features. Further, a cross-branch attention module is applied to improve prediction by fostering the effective information exchange and integration between two branches. Evaluations on the benchmark dataset demonstrate that our method achieves state-of-the-art (SOTA) performance. Specifically, in the PSFH segmentation task, the AoP measurement task, and the AoP classification task, we achieved a DSC of 0.8745 on the PS structure, a <span><math><mi>Δ</mi></math></span>AoP of 7.6743°, and an F1-score of 0.7719, respectively. Compared to the second-ranking method, these results represent improvements of 2.5%, 6.3%, and 1.1%. Our study presents a framework for intrapartum biometry, offering significant advancements in labor monitoring and delivery mode prediction in clinical settings. Future efforts will focus on optimizing AoP-SAM for resource-constrained environments, highlighting its potential for lightweight adaptation. <span><span>https://github.com/maskoffs/AoP-SAM</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"263 ","pages":"Article 125699"},"PeriodicalIF":7.5,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic multi teacher knowledge distillation for semantic parsing in KBQA","authors":"Ao Zou ,&nbsp;Jun Zou ,&nbsp;Shulin Cao ,&nbsp;Jiajie Zhang ,&nbsp;Jinxin Liu ,&nbsp;Jing Wan ,&nbsp;Lei Hou","doi":"10.1016/j.eswa.2024.125599","DOIUrl":"10.1016/j.eswa.2024.125599","url":null,"abstract":"<div><div>Knowledge base question answering (KBQA) is an important task of extracting answers from a knowledge base by analyzing natural language questions. Semantic parsing methods convert natural language questions into executable logical forms to obtain answers on the knowledge base. Conventional approaches often prioritize singular logical forms, overlooking the distinct strengths inherent in various logical frameworks for problem solving. Recognizing that different logical forms may excel in addressing specific types of questions, our aim is to harness these strengths. By integrating the strengths of different logical forms, we expect to achieve more comprehensive and effective semantic parsing solutions. In our paper, we propose a Dynamic Multi Teacher Knowledge Distillation for Semantic Parsing (DMTKD-SP). DMTKD-SP leverages a collection of teacher models, each mastering a unique logical form, to collaboratively guide a student model so that knowledge from different logical forms can be transferred into the student model. To achieve this, we employ a confidence-based weight assignment module to dynamically assign weights for each teacher model. Furthermore, we introduce a self-distillation mechanism to mitigate the confusion caused by simultaneous learning from multiple teachers. We evaluate DMTKD-SP across variants of the KQA Pro dataset, demonstrating an accuracy improvement of 0.35% on five types of questions, with a notable 0.75% improvement for Count questions.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"263 ","pages":"Article 125599"},"PeriodicalIF":7.5,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing cross-domain recommendations: Leveraging personality-based transfer learning with probabilistic matrix factorization","authors":"Somdeep Acharyya,&nbsp;Nargis Pervin","doi":"10.1016/j.eswa.2024.125667","DOIUrl":"10.1016/j.eswa.2024.125667","url":null,"abstract":"<div><div>The conventional method of computing personality scores through extensive questionnaire-based surveys poses practical challenges in real-world scenarios. An alternate route is to predict personality scores from user reviews by analysing various linguistic features such as writing style, word choices, and specific phrases. However, the reviews are domain-dependent and classification models trained on one domain cannot be readily applied to other domains. To mitigate this challenge, we propose a cross-domain recommendation framework called PEMF-CD which leverages a novel mixing strategy to integrate user reviews from multiple domains with common joint embedding space and predict user personality scores using a transformer model. By capturing the underlying semantics and latent representations within the textual data, the transformer architecture can effectively model the linguistic cues to infer users’ personality traits, and the learning is transferred across domains. To further enhance the recommendation process, our model integrates personality-wise and rating pattern-based similarities of users into a probabilistic matrix factorization method that fosters user neighbourhoods based on similarity scores among users. Comprehensive experiments were conducted using five real-world datasets from TripAdvisor and Amazon with varied numbers of users, items, and reviews of up to 44,187, 26,386, and 426,791, respectively. The performance has been benchmarked against thirteen baseline algorithms and the experimental results demonstrate a significant improvements of up to 24.72%, 64.28%, 48.79%, and 61% in RMSE, and 55.9%, 76.7%, 67.6%, and 71.5% in MAE for a 90:10 train–test split with Digital Music, Fashion, Magazine Subscriptions and Video Games datasets from Amazon, respectively. Similar results have been observed for the 80:20 train–test split.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"263 ","pages":"Article 125667"},"PeriodicalIF":7.5,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}