127 results match your criteria Applied Intelligence[Journal]


TERMS: textual emotion recognition in multidimensional space.

Appl Intell (Dordr) 2022 May 11:1-21. Epub 2022 May 11.

Department of Computer Science, National Tsing Hua University, Hsinchu City, Taiwan.

Microblogs generate a vast amount of data in which users express their emotions regarding almost all aspects of everyday life. Capturing affective content from these context-dependent and subjective texts is a challenging task. We propose an intelligent probabilistic model for textual emotion recognition in multidimensional space (TERMS) that captures the subjective emotional boundaries and contextual information embedded in a text for robust emotion recognition. Read More

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Person re-identification via semi-supervised adaptive graph embedding.

Appl Intell (Dordr) 2022 May 11:1-17. Epub 2022 May 11.

Hong Kong Metropolitan University, Hong Kong, Hong Kong.

Video surveillance is an indispensable part of the smart city for public safety and security. Person Re-Identification (Re-ID), as one of elementary learning tasks for video surveillance, is to track and identify a given pedestrian in a multi-camera scene. In general, most existing methods has firstly adopted a CNN based detector to obtain the cropped pedestrian image, it then aims to learn a specific distance metric for retrieval. Read More

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Dynamic stock-decision ensemble strategy based on deep reinforcement learning.

Appl Intell (Dordr) 2022 May 9:1-19. Epub 2022 May 9.

State Key Lab of Software Development Environment, Beihang University, Beijing, 100191 China.

In a complex and changeable stock market, it is very important to design a trading agent that can benefit investors. In this paper, we propose two stock trading decision-making methods. First, we propose a nested reinforcement learning (Nested RL) method based on three deep reinforcement learning models (the Advantage Actor Critic, Deep Deterministic Policy Gradient, and Soft Actor Critic models) that adopts an integration strategy by nesting reinforcement learning on the basic decision-maker. Read More

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MmLwThV framework: A masked face periocular recognition system using thermo-visible fusion.

Appl Intell (Dordr) 2022 May 9:1-17. Epub 2022 May 9.

Indian Institute of Information Technology, Allahabad, India.

In wake of COVID-19, the world has adapted to a new order. People have started wearing mask on their faces to prevent getting infected. The present face recognition models are no longer proving to be efficient in the current circumstances. Read More

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Hashing-based semantic relevance attributed knowledge graph embedding enhancement for deep probabilistic recommendation.

Appl Intell (Dordr) 2022 May 6:1-26. Epub 2022 May 6.

School of Computer Science and Engineering, Central South University, Changsha, 410083 China.

Knowledge graph embedding (KGE) is effectively exploited in providing precise and accurate recommendations from many perspectives in different application scenarios. However, such methods that utilize entire embedded Knowledge Graph (KG) without applying constraints fail to stop the noise penetration into the underlying information. Moreover, higher computational time complexity is a CPU overhead in KG-enhanced systems and applications. Read More

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An empirical study of preprocessing techniques with convolutional neural networks for accurate detection of chronic ocular diseases using fundus images.

Appl Intell (Dordr) 2022 Apr 30:1-19. Epub 2022 Apr 30.

School of Engineering, Ngee Ann Polytechnic, Clementi, 599489 Singapore.

Chronic Ocular Diseases (COD) such as myopia, diabetic retinopathy, age-related macular degeneration, glaucoma, and cataract can affect the eye and may even lead to severe vision impairment or blindness. According to a recent World Health Organization (WHO) report on vision, at least 2.2 billion individuals worldwide suffer from vision impairment. Read More

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Consensus models with aggregation operators for minimum quadratic cost in group decision making.

Appl Intell (Dordr) 2022 Apr 28:1-21. Epub 2022 Apr 28.

Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, 18071 Granada, Spain.

In group decision making (GDM), to facilitate an acceptable consensus among the experts from different fields, time and resources are paid for persuading experts to modify their opinions. Thus, consensus costs are important for the GDM process. Notwithstanding, the unit costs in the common linear cost functions are always fixed, yet experts will generally express more resistance if they have to make more compromises. Read More

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Semantic segmentation in medical images through transfused convolution and transformer networks.

Appl Intell (Dordr) 2022 Apr 25:1-17. Epub 2022 Apr 25.

Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg, South Africa.

Recent decades have witnessed rapid development in the field of medical image segmentation. Deep learning-based fully convolution neural networks have played a significant role in the development of automated medical image segmentation models. Though immensely effective, such networks only take into account localized features and are unable to capitalize on the global context of medical image. Read More

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Low-rank robust online distance/similarity learning based on the rescaled hinge loss.

Appl Intell (Dordr) 2022 Apr 20:1-24. Epub 2022 Apr 20.

Department of Computer Engineering, Hakim Sabzevari University, Sabzevar, Iran.

An important challenge in metric learning is scalability to both size and dimension of input data. Online metric learning algorithms are proposed to address this challenge. Existing methods are commonly based on Passive/Aggressive (PA) approach. Read More

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A novel multi-module integrated intrusion detection system for high-dimensional imbalanced data.

Appl Intell (Dordr) 2022 Apr 14:1-17. Epub 2022 Apr 14.

School of Computer and Software Engineering, Xihua University, Chengdu Sichuan, 610039 China.

The high dimension, complexity, and imbalance of network data are hot issues in the field of intrusion detection. Nowadays, intrusion detection systems face some challenges in improving the accuracy of minority classes detection, detecting unknown attacks, and reducing false alarm rates. To address the above problems, we propose a novel multi-module integrated intrusion detection system, namely GMM-WGAN-IDS. Read More

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MA-Net:Mutex attention network for COVID-19 diagnosis on CT images.

Appl Intell (Dordr) 2022 Apr 9:1-16. Epub 2022 Apr 9.

Department of Pulmonary and Critical Care Medicine, the Affiliated Hospital of Qingdao University, Qingdao, Shandong 266000 China.

COVID-19 is an infectious pneumonia caused by 2019-nCoV. The number of newly confirmed cases and confirmed deaths continues to remain at a high level. RT-PCR is the gold standard for the COVID-19 diagnosis, but the computed tomography (CT) imaging technique is an important auxiliary diagnostic tool. Read More

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Long text feature extraction network with data augmentation.

Appl Intell (Dordr) 2022 Apr 4:1-16. Epub 2022 Apr 4.

Machine Intelligence Research Labs, Scientific Network for Innovation and Research Excellence, Auburn, USA.

The spread of COVID-19 has had a serious impact on either work or the lives of people. With the decrease in physical social contacts and the rise of anxiety on the pandemic, social media has become the primary approach for people to access information related to COVID-19. Social media is rife with rumors and fake news, causing great damage to the Society. Read More

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Monitoring social-distance in wide areas during pandemics: a density map and segmentation approach.

Appl Intell (Dordr) 2022 Apr 5:1-15. Epub 2022 Apr 5.

Center for Research in Mathematics CIMAT AC, campus Zacatecas, Avenida Lasec, Andador Galileo Galilei, Manzana 3 Lote 7, Parque Quantum, Zacatecas, 98160 Mexico.

With the relaxation of the containment measurements around the globe, monitoring the social distancing in crowded public spaces is of great importance to prevent a new massive wave of COVID-19 infections. Recent works in that matter have limited themselves by assessing social distancing in corridors up to small crowds by detecting each person individually, considering the full body in the image. In this work, we propose a new framework for monitoring the social-distance using end-to-end Deep Learning, to detect crowds violating social-distancing in wide areas, where important occlusions may be present. Read More

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A robust fuzzy multi-objective location-routing problem for hazardous waste under uncertain conditions.

Appl Intell (Dordr) 2022 Mar 28:1-21. Epub 2022 Mar 28.

Faculty of Industrial Engineering, Urmia University of Technology, Urmia, 57166 Iran.

Industrialization and population growth have been accompanied by many problems such as waste management worldwide. Waste management and reduction have a vital role in national management. The presents study represents a multi-objective location-routing problem for hazardous wastes. Read More

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Driving maneuver classification from time series data: a rule based machine learning approach.

Appl Intell (Dordr) 2022 Mar 28:1-16. Epub 2022 Mar 28.

School of Computing and Information Systems, Faculty of Science and Technology, Athabasca University, Athabasca, AB Canada.

Drivers' improper driving behavior plays a vital role in road accidents. Different approaches have been proposed to classify and evaluate driving performance to ensure road safety. However, most of the techniques are based on neural networks which work like a black box and make the logical reasoning behind the classification decision unclear. Read More

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Improving exchange rate forecasting via a new deep multimodal fusion model.

Appl Intell (Dordr) 2022 Mar 25:1-17. Epub 2022 Mar 25.

School of Economics, Hefei University of Technology, Hefei, China.

Exchange rates are affected by the impact of disparate types of new information as well as the couplings between these modalities. Previous work mainly predicted exchange rates solely based on market indicators and therefore achieved unsatisfactory results. In response to such an issue, this study develops an inventive multimodal fusion-based long short-term memory (MF-LSTM) model to forecast the USD/CNY exchange rate. Read More

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Emoji use in China: popularity patterns and changes due to COVID-19.

Appl Intell (Dordr) 2022 Mar 21:1-11. Epub 2022 Mar 21.

College of Systems Engineering, National University of Defense Technology, Changsha, 410073 China.

Emojis are small pictograms that are frequently embedded within micro-texts to more directly express emotional meanings. To understand the changes in the emoji usage of internet users during the COVID-19 outbreak, we analysed a large dataset collected from Weibo, the most popular Twitter-like social media platform in China, from December 1, 2019, to March 20, 2020. The data contained 38,183,194 microblog posts published by 2,239,472 unique users in Wuhan. Read More

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Three-dimensional DenseNet self-attention neural network for automatic detection of student's engagement.

Appl Intell (Dordr) 2022 Mar 18:1-21. Epub 2022 Mar 18.

Academy of Scientific and Innovative Research(AcSIR), Ghaziabad, India.

Today, due to the widespread outbreak of the deadly coronavirus, popularly known as COVID-19, the traditional classroom education has been shifted to computer-based learning. Students of various cognitive and psychological abilities participate in the learning process. However, most students are hesitant to provide regular and honest feedback on the comprehensiveness of the course, making it difficult for the instructor to ensure that all students are grasping the information at the same rate. Read More

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Mining sequential patterns with flexible constraints from MOOC data.

Appl Intell (Dordr) 2022 Mar 23:1-17. Epub 2022 Mar 23.

College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060 China.

Online learning is playing an increasingly important role in education. Massive open online course (MOOC) platforms are among the most important tools in online learning, and record historical learning data from an extremely large number of learners. To enhance the learning experience, a promising approach is to apply sequential pattern mining (SPM) to discover useful knowledge in these data. Read More

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A large-scale group decision making method to select the ideal mobile health application for the hospital.

Appl Intell (Dordr) 2022 Mar 18:1-21. Epub 2022 Mar 18.

School of Business, Central South University, Changsha, 410083 China.

Mobile health, which is not limited by time and space, can effectively alleviate the imbalance of medical resources. Currently, more and more hospitals begin to pay attention to online medical care and actively expand their mobile channels. Among of which, the cooperation with the third-party platform is an effective way to expand the online services of most hospitals. Read More

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Confidence interval for micro-averaged and macro-averaged scores.

Appl Intell (Dordr) 2022 Mar 31;52(5):4961-4972. Epub 2021 Jul 31.

Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA.

A binary classification problem is common in medical field, and we often use sensitivity, specificity, accuracy, negative and positive predictive values as measures of performance of a binary predictor. In computer science, a classifier is usually evaluated with precision (positive predictive value) and recall (sensitivity). As a single summary measure of a classifier's performance, score, defined as the harmonic mean of precision and recall, is widely used in the context of information retrieval and information extraction evaluation since it possesses favorable characteristics, especially when the prevalence is low. Read More

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CBRW: a novel approach for cancelable biometric template generation based on 1-D random walk.

Appl Intell (Dordr) 2022 Mar 15:1-19. Epub 2022 Mar 15.

National Institute of Technology, Uttarakhand, India.

Cancelable Biometric is a challenging research field in which security of an original biometric image is ensured by transforming the original biometric into another irreversible domain. Several approaches have been suggested in literature for generating cancelable biometric templates. In this paper, two novel and simple cancelable biometric template generation methods based on Random Walk (CBRW) have been proposed. Read More

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A heterogeneous multi-attribute case retrieval method based on neutrosophic sets and TODIM for emergency situations.

Appl Intell (Dordr) 2022 Mar 11:1-16. Epub 2022 Mar 11.

Institute of Decision Science, Fuzhou University, Fuzhou, 350116 Fujian China.

Heterogeneous multi-attribute case retrieval is a crucial step in generating emergency alternatives during the course of emergency decision making (EDM) by referring to historical cases. This paper develops a heterogeneous multi-attribute case retrieval method for EDM that considers five attribute formats: crisp numbers, interval numbers, intuitionistic fuzzy numbers, single-valued neutrosophic numbers (SvNNs), and interval-valued neutrosophic numbers (IvNNs). First, we propose a similarity measurement of IvNNs and calculate the attribute similarities for the five attribute formats. Read More

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A Cluster-based Stratified Hybrid Decision Support Model under Uncertainty: Sustainable Healthcare Landfill Location Selection.

Appl Intell (Dordr) 2022 Mar 7:1-20. Epub 2022 Mar 7.

School of Business and Economics, RWTH Aachen University, 52072 Aachen, Germany.

Nowadays, healthcare waste management has become one of the significant environmental, health, and social problems. Due to population and urbanization growth and an increase in healthcare waste disposals according to the growing number of diseases and pandemics like COVID-19, disposal of healthcare waste has become a critical issue. Authorities in big cities require reliable decision support systems to empower them to make strategic decisions to provide safe disposal methods with a prospective vision. Read More

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Online education satisfaction assessment based on cloud model and fuzzy TOPSIS.

Appl Intell (Dordr) 2022 Mar 5:1-16. Epub 2022 Mar 5.

College of Computer and Cyber Security, Fujian Normal University, Fuzhou, Fujian 350117 China.

During the COVID-19, colleges organized online education on a massive scale. To make better use of online education in the post-epidemic era, this paper conducts an online education satisfaction survey with four types of colleges and 129,325 students propose a fuzzy TOPSIS (technique for order preference by similarity to ideal solution) method based on the cloud model to rank the satisfaction of different colleges. Firstly, based on the characteristics of online education during the COVID-19, we build an evaluation indicator system from four dimensions: technology, instructor, learner and environment including, 10 indicators and 94 sub-indicators. Read More

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An edge-driven multi-agent optimization model for infectious disease detection.

Appl Intell (Dordr) 2022 Mar 7:1-12. Epub 2022 Mar 7.

Western Norway University of Applied Sciences, Bergen, Norway.

This research work introduces a new intelligent framework for infectious disease detection by exploring various emerging and intelligent paradigms. We propose new deep learning architectures such as entity embedding networks, long-short term memory, and convolution neural networks, for accurately learning heterogeneous medical data in identifying disease infection. The multi-agent system is also consolidated for increasing the autonomy behaviours of the proposed framework, where each agent can easily share the derived learning outputs with the other agents in the system. Read More

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Machine learning techniques applied to mechanical fault diagnosis and fault prognosis in the context of real industrial manufacturing use-cases: a systematic literature review.

Appl Intell (Dordr) 2022 Mar 4:1-35. Epub 2022 Mar 4.

GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Polytechnic of Porto (ISEP/IPP), Porto, Portugal.

When put into practice in the real world, predictive maintenance presents a set of challenges for fault detection and prognosis that are often overlooked in studies validated with data from controlled experiments, or numeric simulations. For this reason, this study aims to review the recent advancements in mechanical fault diagnosis and fault prognosis in the manufacturing industry using machine learning methods. For this systematic review, we searched Web of Science, ACM Digital Library, Science Direct, Wiley Online Library, and IEEE Xplore between January 2015 and October 2021. Read More

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Social-path embedding-based transformer for graduation development prediction.

Appl Intell (Dordr) 2022 Mar 3:1-18. Epub 2022 Mar 3.

School of Computer Science, Hunan University, Hunan, China.

As the education of students attracts more and more attention, the task of graduation development prediction has gradually become a hot topic in academia and industry. The task of graduation development prediction aims to predict the employment category of students in advance via academic achievement data, which can help administrators understand students' learning status and set up a reasonable learning plan. However, existing research ignores the potential impact of social relationships on students' graduation development choices. Read More

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A theoretical analysis based on causal inference and single-instance learning.

Appl Intell (Dordr) 2022 Feb 28:1-14. Epub 2022 Feb 28.

Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, China.

Although using single-instance learning methods to solve multi-instance problems has achieved excellent performance in many tasks, the reasons for this success still lack a rigorous theoretical explanation. In particular, the potential relation between the number of causal factors (also called causal instances) in a bag and the model performance is not transparent. The goal of our study is to use the causal relationship between instances and bags to enhance the interpretability of multi-instance learning. Read More

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February 2022

Twitter-aided decision making: a review of recent developments.

Appl Intell (Dordr) 2022 Feb 26:1-16. Epub 2022 Feb 26.

Graduate School of Information Science and Technology, Osaka University, Osaka, Japan.

Twitter is one of the largest online platforms where people exchange information. In the first few years since its emergence, researchers have been exploring ways to use Twitter data in various decision making scenarios, and have shown promising results. In this review, we examine 28 newer papers published in last five years (since 2016) that continued to advance Twitter-aided decision making. Read More

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February 2022