Journal of Ambient Intelligence and Humanized Computing最新文献

{"title":"An extended COPRAS method based on complex q-rung orthopair fuzzy 2-tuple linguistic Maclaurin symmetric mean aggregation operators","authors":"Sumera Naz, Rida Mehreen, Tahir Abbas, Gabriel Piñeres-Espitia, Shariq Aziz Butt","doi":"10.1007/s12652-023-04742-2","DOIUrl":"https://doi.org/10.1007/s12652-023-04742-2","url":null,"abstract":"<p>The complex <i>q</i>-rung orthopair fuzzy 2-tuple linguistic set (C<i>q</i>-ROFTLS), which merges the concepts of complex <i>q</i>-rung orthopair fuzzy sets (C<i>q</i>-ROFS) and 2-tuple linguistic terms, offers significant advantages in dealing with uncertain and imprecise information during decision-making by effectively representing two-dimensional information within a single set. Notably, the C<i>q</i>-ROFTLS introduces phase terms that empower experts to express their perspectives flexibly, particularly enhancing its capacity to address periodic elements. To address uncertainty, this approach employs complex values to quantify both membership and non-membership degrees within 2-tuple linguistic environment. Additionally, this research introduces the generalized Maclaurin symmetric mean (MSM) aggregation operator, specifically designed for C<i>q</i>-ROFTL information. This introduces the C<i>q</i>-ROFTLMSM and its dual form, the C<i>q</i>-ROFTL Dual MSM (C<i>q</i>-ROFTLDMSM), each carrying valuable properties. In cases where the importance of input factors varies, the study proposes the C<i>q</i>-ROFTL weighted MSM (C<i>q</i>-ROFTLWMSM) and its dual form, the C<i>q</i>-ROFTL weighted dual MSM (C<i>q</i>-ROFTLWDMSM). These operators not only make their debut but also showcase their properties and applications. They flexibly adjust to the significance of inputs, leading to a more refined decision-making process. The methodology extends to address multi-attribute group decision-making (MAGDM) within the C<i>q</i>-ROFTL framework using the Complex Proportional Assessment (COPRAS) method. The introduction of new aggregation techniques further enhances this approach. A practical illustration involving the selection of the optimal bio-energy production technology (BPT) highlights the real-world effectiveness of the methodology. Through thorough comparisons and a focused exploration of advantages, the study effectively validates the merits of this approach.</p>","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140152544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Design of students’ learning state evaluation model in online education based on double improved neural network","authors":"Huaying Zhang","doi":"10.1007/s12652-024-04765-3","DOIUrl":"https://doi.org/10.1007/s12652-024-04765-3","url":null,"abstract":"<p>In today's highly developed era of information technology, online education is gradually becoming an important teaching mode. Online education provides convenient learning resources and flexible learning methods through online platforms, allowing students to learn according to their own schedule and learning needs. However, compared to traditional education, online education faces some challenges, one of which is how to accurately assess students' learning status. Design an online education student learning status evaluation model based on dual improved neural networks with the aim of improving student learning effectiveness. Using systematic clustering statistical methods to preliminarily analyze the influencing factors of online education students' learning status, and construct an initial evaluation index system; Using the Apriori algorithm to filter the initial indicators, a final online education student learning status evaluation index system is constructed. Using wavelet denoising method to remove noise from evaluation index data, a dual improved radial basis function neural network model is constructed as input. Determine the number of hidden layers in the network using the K-means clustering algorithm, thereby determining the network structure; Based on the optimal network structure, the state transition algorithm is used to adjust the network parameters, and the trained neural network is used for online education student learning state evaluation, outputting the final evaluation result of online education student learning state. The experimental results show that the contribution rate of the model's indicator information reaches 93%, which can accurately evaluate the learning status of online education students based on the optimal model structure and parameters. The above results demonstrate that the constructed model can help teachers and students understand students' learning needs and difficulties in real-time, and provide corresponding teaching support and guidance to promote personalized teaching and improve students' learning experience and outcomes.</p>","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":"108 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140152546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Failure recovery mechanism for BDI agents based on abilities and discovery protocols","authors":"Hichem Baitiche, Mourad Bouzenada, Djamel Eddine Saidouni","doi":"10.1007/s12652-024-04754-6","DOIUrl":"https://doi.org/10.1007/s12652-024-04754-6","url":null,"abstract":"<p>An ambient agent may fail to achieve its goal due to the dynamism and nondeterminism of the environment. Based on the concepts of <i>Abilities</i> and <i>Discovery Protocols</i>, we present a context aware failure recovery mechanism for Belief-desire-intention agents. Using a based STRIPS Planner, our approach recovers the failed plan by repairing or replacing faulty actions. Compared to existing works generates the recovery plans dynamically at runtime according to the current context. Indeed, agent abilities are generated and maintained dynamically. To our acknowledge, this is the first time that a dynamic set of abilities has been used in failure recovery.</p>","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140152448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 review on visible-light eye-tracking methods based on a low-cost camera","authors":"Alberto J. Molina-Cantero, Clara Lebrato-Vázquez, Juan A. Castro-García, Manuel Merino-Monge, Félix Biscarri-Triviño, José I. Escudero-Fombuena","doi":"10.1007/s12652-024-04760-8","DOIUrl":"https://doi.org/10.1007/s12652-024-04760-8","url":null,"abstract":"<p>This paper is the first of a two-part study aiming at building a low-cost visible-light eye tracker (ET) for people with amyotrophic lateral sclerosis (ALS). The whole study comprises several phases: (1) analysis of the scientific literature, (2) selection of the studies that better fit the main goal, (3) building the ET, and (4) testing with final users. This document basically contains the two first phases, in which more than 500 studies, from different scientific databases (IEEE Xplore, Scopus, SpringerLink, etc.), fulfilled the inclusion criteria, and were analyzed following the guidelines of a scoping review. Two researchers screened the searching results and selected 44 studies (-value = 0.86, Kappa Statistic). Three main methods (appearance-, feature- or model- based) were identified for visible-light ETs, but none significantly outperformed the others according to the reported accuracy -<i>p</i> = 0.14, Kruskal–Wallis test (KW)-. The feature-based method is abundant in the literature, although the number of appearance-based studies is increasing due to the use of deep learning techniques. Head movements worsen the accuracy in ETs, and only a very few numbers of studies considered the use of algorithms to correct the head pose. Even though head movements seem not to be a big issue for people with ALS, some slight head movements might be enough to worsen the ET accuracy. For this reason, only studies that did not constrain the head movements with a chinrest were considered. Five studies fulfilled the selection criteria with accuracies less than <span>(2^{circ })</span>, and one of them is illuminance invariant.</p>","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":"69 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140156678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 review on devices and learning techniques in domestic intelligent environment","authors":"Jiancong Ye, Mengxuan Wang, Junpei Zhong, Hongjie Jiang","doi":"10.1007/s12652-024-04759-1","DOIUrl":"https://doi.org/10.1007/s12652-024-04759-1","url":null,"abstract":"<p>With the rapid development and wide proliferation of sensor devices and the Internet of Things (IoT), machine learning algorithms processing and analysing one or more modalities of sensory signals have become an active research field given its numerous applications, particularly in the domestic intelligent environment (DIE). In the past decades, the research on sensing and interactive devices of DIE and deep learning (DL) based methods have become strikingly popular. Several missions, such as the pro- cessing and analysis of sensing signals related to domestic instruments and the control of certain devices to act upon the results, comprise the main working targets in DIE. The goal of this review is to provide a brief overview of the aforementioned sensors, their related DL algorithms and their applications. To comprehend the ideas behind the use of various devices found in domestic intelligent instruments, we first summarize the available information. Then, to quantify and adapt the residents’ knowledge of the household environment, we review data-driven learning techniques based on the aforementioned sensor-based devices and introduce robotic applications that provide helpers and action outputs in the environment. Finally, we investigate the commonly utilized datasets relevant to DIE and human activ- ity recognition (HAR) and explore the challenges and prospects of their applications in the DIE field.</p>","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":"36 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140124306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Comparative study of information measures in portfolio optimization problems","authors":"Luckshay Batra, H. C. Taneja","doi":"10.1007/s12652-024-04766-2","DOIUrl":"https://doi.org/10.1007/s12652-024-04766-2","url":null,"abstract":"<p>This paper presents a rich class of information theoretical measures designed to enhance the accuracy of portfolio risk assessments. The Mean-Variance model, pioneered by Harry Markowitz, revolutionized the financial sector as the first formal mathematical method to risk-averse investing in portfolio optimization theory. We analyze the effectiveness of this with the models that replace expected portfolio variance with measures of information (uncertainty of the portfolio allocations to the different assets) and five major practical issues. The empirical analysis is carried out on the historical data of Indian financial stock indices by application of portfolio optimization problem with information measures as the objective function and constraints derived from the return and the risk. Our findings indicate that the information measures with parameters can be used as an adequate supplement to traditional portfolio optimization models such as the mean-variance model.</p>","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140124313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Special issue on infodemics","authors":"David Camacho, Juan Gómez-Romero, Jason J. Jung","doi":"10.1007/s12652-024-04784-0","DOIUrl":"https://doi.org/10.1007/s12652-024-04784-0","url":null,"abstract":"<p>In this editorial, we explore the urgent challenges created by the rise of infodemics —a term used to describe the <i>epidemic</i> spread of fake news, misinformation, and disinformation through social networks initially associated with the COVID-19 pandemic. This issue has drawn significant attention from various academic fields, including computer science, artificial intelligence, mathematics, physics, biology, sociology, and psychology, among others. This special issue is dedicated to advancing infodemics research across various academic domains. The selected papers include relevant contributions advancing the state of the art in the area, ranging from network analysis for identifying influential nodes and communities in networks to language processing for text classification and filtering relevant messages within extensive corpora.</p>","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":"91 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140129866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"TIDAL: exploring the potential of data physicalization-based interactive environment on runners' motivation","authors":"Mengyan Guo, Jun Hu, Steven Vos","doi":"10.1007/s12652-024-04762-6","DOIUrl":"https://doi.org/10.1007/s12652-024-04762-6","url":null,"abstract":"<p>Representing fitness-related data physically can better help people gain awareness and reflect on their physical activity behavior. However, there has been limited research conducted on the impact of physicalizing personal data in a public context, particularly regarding its effect on motivations for physical activity. Augmenting the physical environment with interactive technology holds great promise in facilitating outdoor physical activity. To explore the design space of data physicalization-based interactive environments, we created TIDAL, a design concept that provides physical rewards in the form of tiles on the road to acknowledge runners’ goal achievements. We created a video prototype as a probe to gather insights through semi-structured interviews with six recreational runners to evaluate TIDAL. The co-constructing stories method, a participatory design technique, was employed during these interviews to facilitate qualitative evaluation. The results of our study showed that TIDAL has the potential to increase runners’ motivation. We reported the key insights derived from participants’ feedback and co-constructed stories and discussed the broader implications of our work.</p>","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140129830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Adversarial graph node classification based on unsupervised learning and optimized loss functions","authors":"","doi":"10.1007/s12652-024-04768-0","DOIUrl":"https://doi.org/10.1007/s12652-024-04768-0","url":null,"abstract":"<h3>Abstract</h3> <p>The research field of this paper is unsupervised learning in machine learning, aiming to address the problem of how to simultaneously resist feature attacks and improve model classification performance in unsupervised learning. For this purpose, this paper proposes a method to add an optimized loss function after the graph encoding and representation stage. When the samples are relatively balanced, we choose the cross-entropy loss function for classification. When difficult-to-classify samples appear, an optimized Focal Loss*() function is used to adjust the weights of these samples, to solve the problem of imbalanced positive and negative samples during training. The developed method achieved superior performance accuracy with the values of 0.721 on the Cora dataset, 0.598 on the Citeseer dataset,0.862 on the Polblogs dataset. Moreover, the testing accuracy value achieved by optimized model is 0.745, 0.627, 0.892 on the three benchmark datasets, respectively. Experimental results show that the proposed method effectively improves the robustness of adversarial training models in downstream tasks and reduces potential interference with original data. All the test results are validated with the k-fold cross validation method in order to make an assessment of the generalizability of these results.</p>","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140124387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Machine learning for physical motion identification using EEG signals: a comparative study of classifiers and hyperparameter tuning","authors":"Poh Foong Lee, Kah Yoon Chong","doi":"10.1007/s12652-024-04764-4","DOIUrl":"https://doi.org/10.1007/s12652-024-04764-4","url":null,"abstract":"<p>This study addresses the crucial task of accurately classifying brainwave signals associated with distinct brain states, utilizing five supervised machine learning algorithms: K-Nearest Neighbors, Support Vector Machine, Decision Tree, Linear Discriminant Analysis, and Logistics Regression. The primary objectives encompass developing and optimizing these models, assessing the impact of hyperparameter tuning on performance through metrics like accuracy, consistency, and prediction time, and creating a user-friendly web-based deployment interface. The Decision Tree model emerges with the highest average accuracy score of 90.03%, swift prediction times, and notable consistency. Following hyperparameter tuning, SVM and LR showcase substantial accuracy gains (15.63% and 1.50% respectively), enhancing all models' consistency. KNN and SVM are identified as the top-performing algorithms for accurate brain state classification. This study's findings hold implications for neuroscience research, human–computer interaction, healthcare diagnostics, and assistive technologies, offering insights into both effective algorithm selection and the role of hyperparameter tuning in optimizing model performance.</p>","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":"28 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140124310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}