Traditional clustering algorithms can be applied for the pre-sorting step of radar signal sorting. It can effectively dilute the pulse stream and prevent the dense pulse stream from interfering pulse repetition interval (PRI) extraction. However, the pre-sorting deviation will cause interference and missing pulses during the main sorting process. To solve this problem, we deploy the unmanned aerial

With the rapid development of social media, reliable information released by the public on social media can provide important decision-making support. Therefore, the consideration of the public as another decision-making body participating in large-scale group decision-making (LSGDM) problems has become an extensively researched topic. However, the participation of the public as a decision-making body

The risk assessment is one of the most significant procedures for identifying, preventing, and controlling Occupational Health and Safety (OHS) risks. One of many kinds of techniques for OHS risk assessment is based on the Fine-Kinney model. Most of the Fine-Kinney-based risk assessment approaches can consider the relative importance degree of risk parameters. Nevertheless, the current Fine-Kinney-based

Multiple and heterogenous Earth observation (EO) platforms are broadly used for a wide array of applications, and the integration of these diverse modalities facilitates better extraction of information than using them individually. The detection capability of the multispectral unmanned aerial vehicle (UAV) and satellite imagery can be significantly improved by fusing with ground hyperspectral data

Gait abnormalities are typically derived from neurological conditions or orthopaedic problems and can cause severe consequences such as limited mobility and falls. Gait analysis plays a crucial role in monitoring gait abnormalities and discovering underlying deficits can help develop rehabilitation programs. Contemporary gait analysis requires a multi-modal gait analysis approach where spatio-temporal

Consensus reaching is a key issue in group decision-making, because conflicts of interest among groups are common. Democratic consensus refers to achieve a soft consensus among collective as well as ensure the effective participation and satisfaction of individuals. Multi-person multi-criteria large scale decision making (MpMcLSDM) usually involves a huge number of decision makers (DMs/participants)

We investigate the remote state estimation problem for networked systems over parallel noise-free communication channels. Due to limited network capabilities in practical network environments, communication schedulers are implemented at the transmit side of each subchannel to promote resource efficiency. Specifically, the processed signals are transmitted only when it is necessary to provide the real-time

For the past decades there has been a rising interest for wireless sensor networks to obtain information about an environment. One interesting modality is that of audio, as it is highly informative for numerous applications including speech recognition, urban scene classification, city monitoring, machine listening and classifying domestic activities. However, as they operate at prohibitively high

We introduce the concept of a fuzzy measure μ on a set X. We discuss some of the properties of a fuzzy measure. We provide some notable examples of fuzzy measures. We discuss the important application of using fuzzy measure to provide information about an uncertain variable V. Here the measure of a subset A indicates the anticipation of finding the value of V in A. Our interest here is in finding the

This study focuses on linguistic information operational realization through information granulation in group decision-making (GDM) scenarios where the preference information offered by decision-makers over alternatives is described using distributed linguistic preference relations (DLPRs). First, an information granulation model is proposed to arrive at the operational realization of linguistic information

The conventional diagnostic process and tools of cardiovascular autonomic neuropathy (CAN) can easily identify the two main categories of the condition: severe/definite CAN and normal/healthy without CAN. Conventional techniques encounter significant challenges when identifying CAN in its early or atypical stages due to the inherent imbalanced and incompleteness condition in the collected clinical

Establishing reliable correspondences by a deep neural network is an important task in computer vision, and it generally requires permutation-equivariant architecture and rich contextual information. In this paper, we design a Permutation-Equivariant Split Attention Network (called PESA-Net), to gather rich contextual information for the feature matching task. Specifically, we propose a novel “Spl

Multi-channel and single-channel image denoising are on two important development fronts. Integrating multi-channel and single-channel image denoisers for further improvement is a valuable research direction. A natural assumption is that using more useful information is helpful to the output results. In this paper, a novel multi-channel and single-channel fusion paradigm (MSF) is proposed. The proposed

By now people’s opinions and actions are more and more strongly influenced by what is posted and shared on the various social networks. Thus, malicious users can purposely manipulate other users posting fake news/reviews. In order to face this challenge, modern online social networks are beginning to adopt tool for user trustworthiness assessment. Current assessment solutions mainly adopt multi-criteria

Polarization imaging can retrieve inaccurate objects’ 3D shapes with fine textures, whereas coarse but accurate depths can be provided by binocular stereo vision. To take full advantage of these two complementary techniques, we investigate a novel 3D reconstruction method based on the fusion of polarization imaging and binocular stereo vision for high quality 3D reconstruction. We first generate the

Explainable Artificial Intelligence (XAI) is an emerging research topic of machine learning aimed at unboxing how AI systems’ black-box choices are made. This research field inspects the measures and models involved in decision-making and seeks solutions to explain them explicitly. Many of the machine learning algorithms cannot manifest how and why a decision has been cast. This is particularly true

Graph Convolutional Networks (GCNs) are widely applied in classification tasks by aggregating the neighborhood information of each sample to output robust node embedding. However, conventional GCN methods do not update the graph during the training process so that their effectiveness is always influenced by the quality of the input graph. Moreover, previous GCN methods lack the interpretability to

This study proposes a non-threshold consensus model that combines the minimum cost and maximum consensus-increasing for multi-attribute large group decision-making (MALGDM). First, the large-scale experts is classified into several clusters via the combination of the similarities of evaluation information, unit consensus cost, and adjustment willingness. Then, a more sensitive consensus measure method

Ensemble learning – the application of multiple learning models on the same task – is a common technique in multiple domains. While employing multiple models enables reaching higher classification accuracy, this process can be time consuming, costly, and make scaling more difficult. Given that each model may have different capabilities and costs, assigning the most cost-effective set of learners for

Deep Learning and its applications have cascaded impactful research and development with a diverse range of modalities present in the real-world data. More recently, this has enhanced research interests in the intersection of the Vision and Language arena with its numerous applications and fast-paced growth. In this paper, we present a detailed overview of the latest trends in research pertaining to

Emotion recognition is a crucial application in the human–computer interaction. It is usually performed by using facial expressions as the main modality, which may not be reliable. In this study, we proposed a multimodal approach that uses 2-channel electroencephalography (EEG) signals and the eye modality in addition to the face modality to enhance the recognition performance. We also studied the

Due to the proliferation of biomedical imaging modalities, such as Photo-acoustic Tomography, Computed Tomography (CT), Optical Microscopy and Tomography, etc., massive amounts of data are generated on a daily basis. While massive biomedical data sets yield more information about pathologies, they also present new challenges of how to fully explore the data. Data fusion methods are a step forward towards

In this paper, we consider the problem of fusion of synthetic aperture radar (SAR) images from spaceborne and airborne sensors and investigate its applications to inshore ship target detection. Existing SAR image fusion methods mainly focus on image denoising or texture enhancement, but show limited improvement of target-to-clutter ratios (TCRs) in composite images and lead to deteriorated target detection

Smart healthcare is a framework that utilizes technologies such as wearable devices, the Internet of Medical Things (IoMT), sophisticated machine learning algorithms, and wireless communication technology to seamlessly access health records, link individuals, resources, and organizations, and then effectively handle and react to health environment demands intelligently. One of the main ingredients

Big data has been continuously generated from the rapidly developing of cloud/fog/edge computing, Internet of Things (IoT) and 5G technology. This big data not only brings great benefits and opportunities to human beings but also brings many risks and challenges. One major challenge is how to represent and treat higher-order and heterogeneous data from multi-sources. Tensors are emerging as powerful

Image fusion, which refers to extracting and then combining the most meaningful information from different source images, aims to generate a single image that is more informative and beneficial for subsequent applications. The development of deep learning has promoted tremendous progress in image fusion, and the powerful feature extraction and reconstruction capabilities of neural networks make the

This paper is concerned with the state estimation problem for delayed complex dynamic networks with non-identical local dynamical systems. The state estimation is conducted based on constrained information of the measurement outputs. Specifically, the network outputs are available only from a portion of network nodes, and such outputs are transmitted from the network nodes to the estimator in an intermittent

The modern technological advancement influences the growth of the cyber–physical system and cyber–social system to a more advanced computing system cyber–physical–social system (CPSS). Therefore, CPSS leads the data science revolution by promoting tri-space information resource from a single space. The establishment of CPSSs increases the related privacy concerns. To provide privacy on CPSSs data,

Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of uncertainties during both optimization and decision making processes. They have been applied to solve a variety of real-world problems in science and engineering. Bayesian approximation and ensemble learning techniques are two widely-used types of uncertainty quantification (UQ) methods. In this regard, researchers

Deep neural nets (DNNs) mostly tend to outperform other machine learning (ML) approaches when the training data is abundant, high-dimensional, sparse, or consisting of raw data (e.g., pixels). For datasets with other characteristics—for example, dense tabular numerical data—algorithms such as Gradient Boosting Machines and Random Forest often achieve comparable or better performance at a fraction of

In social networks, factors that influence the spread of information are essential for companies to comprehend. This study uses the opinion dynamic theory to investigate the influence of multiple advertisement opinion leaders in social networks. We construct an integrated bounded confidence model to simulate the evolution of followers’ opinions under two advertisement opinion leaders. Through experimental

Deep learning has emerged as a powerful machine learning technique to employ in multimodal sentiment analysis tasks. In the recent years, many deep learning models and various algorithms have been proposed in the field of multimodal sentiment analysis which urges the need to have survey papers that summarize the recent research trends and directions. This survey paper tackles a comprehensive overview

Benefitting from the strong feature extraction capability of deep learning, infrared and visible image fusion has made a great progress. Since infrared and visible images are obtained by different sensors with different imaging mechanisms, there exists domain discrepancy, which becomes stumbling block for effective fusion. In this paper, we propose a novel self-supervised feature adaption framework

Existing image fusion methods always use the same representations for different modal medical images. Otherwise, they solve the fusion problem by subjectively defining characteristics to be preserved. However, it leads to the distortion of unique information and restricts the fusion performance. To address the limitations, this paper proposes an unsupervised enhanced medical image fusion network. We

Recently, smart data has attracted great attention in the smart city community since it can provide valuable information to support intelligent services such as planning, monitoring, and decision making. However, it imposes a big challenge to explore smart data from big data gathered from smart city with various advanced fusion and analysis approaches. This paper proposes an incremental tensor-based

Cross-domain word representation aims to learn high-quality semantic representations in an under-resourced domain by leveraging information in a resourceful domain. However, most existing methods mainly transfer the semantics of common words across domains, ignoring the semantic relations among domain-specific words. In this paper, we propose a domain structure-based transfer learning method to learn

The purpose of this paper is to implement a computer-aided diagnosis (CAD) system for multiple sclerosis (MS) based on analysing the outer retina as assessed by multifocal electroretinograms (mfERGs). MfERG recordings taken with the RETI‐port/scan 21 (Roland Consult) device from 15 eyes of patients diagnosed with incipient relapsing-remitting MS and without prior optic neuritis, and from 6 eyes of

This article proposes a bidirectional feedback mechanism for consensus in group decision making (GDM) driven by the behavior of decision makers (DMs), which is discriminated with a flexible harmony degree as one of three possible states: (1) ‘tolerance behavior’; (2) ‘rationalist behavior’; and (3) ‘conflict behavior’. The first two states are possible to be resolved in the consensus reaching process

High-Utility Itemset Mining (HUIM) is considered a major issue in recent decades since it reveals profit strategies for use in industry for decision-making. Most existing works have focused on mining high-utility itemsets from databases showing large amount of patterns; however exact decisions are still challenging to make from that large amounts of discovered knowledge. Closed High-utility itemset

The complexity of linguistic distribution assessments increases the difficulty for the decision makers dealing with them. Recently, stochastic dominance has been varied to be a useful tool to compare two stochastic variables. Inspired by this, in this paper we dedicate to utilizing the stochastic dominance to compare the linguistic distribution assessments and further discuss the consensus reaching

The sudden increase in coronavirus disease 2019 (COVID-19) cases puts high pressure on healthcare services worldwide. At this stage, fast, accurate, and early clinical assessment of the disease severity is vital. In general, there are two issues to overcome: (1) Current deep learning-based works suffer from multimodal data adequacy issues; (2) In this scenario, multimodal (e.g., text, image) information

The number of Internet of Things devices generating data streams is expected to grow exponentially with the support of emergent technologies such as 5G networks. Therefore, the online processing of these data streams requires the design and development of suitable machine learning algorithms, able to learn online, as data is generated. Like their batch-learning counterparts, stream-based learning algorithms

A traditional neural network cannot realize the invariance of image rotation and distortion well, so an attacker can fool the neural network by adding tiny disturbances to an image. If traffic signs are attacked, automatic driving will probably be misguided, leading to disastrous consequences. Inspired by the principle of human vision, this paper proposes a defense method based on an attentional mechanism

Aggregation functions, which are at the heart of a number of information fusion processes, allow summarization of multiple inputs into a single representative value. Extended aggregation functions are defined such that the input data can be of varying cardinality, with the implication that there is some consistency across the methods of calculation. This article formalizes an approach to extended aggregation

The great popularity of smartphones, together with the increasingly important aim of providing context-aware services, has spurred interest in developing indoor tracking systems. Accurate tracking and localization systems are seen as key services for most context-aware applications. Research projects making use of radio signals detected by radio interfaces and the data captured by sensors commonly

Multi-modal fusion has been proved to help enhance the performance of scene classification tasks. This paper presents a 2D–3D Fusion stage that combines 3D Geometric Features with 2D Texture Features obtained by 2D Convolutional Neural Networks. To get a robust 3D Geometric embedding, a network that uses two novel layers is proposed. The first layer, Multi-Neighbourhood Graph Convolution, aims to learn

In this paper, we compare and evaluate different testing protocols used for automatic COVID-19 diagnosis from X-Ray images in the recent literature. We show that similar results can be obtained using X-Ray images that do not contain most of the lungs. We are able to remove the lungs from the images by turning to black the center of the X-Ray scan and training our classifiers only on the outer part

Lightweight and low-cost wearable magnetic and inertial measurement units (MIMUs) have found numerous applications, such as aerial vehicle navigation or human motion analysis, where the 3D orientation tracking of a rigid body is of interest. However, due to the errors in measurements of gyroscope, accelerometer, and/or magnetometer inside a MIMU, numerous studies have proposed sensor fusion algorithms

Due to recent advances in the film industry, the production of movies has grown exponentially, which has led to challenges in what is referred to as discoverability: given the overwhelming number of choices, choosing which film to watch has become a tedious task for audiences. Movie summarization (MS) could help, as it presents the central theme of the movie in a compact format and makes browsing more

Diabetes is a global epidemic. Long-term exposure to hyperglycemia can cause chronic damage to various tissues. Thus, early diagnosis of diabetes is crucial. In this study, we designed a computational system to predict diabetes risk by fusing multifarious types of physical examination data. We collected 1,507,563 physical examination data of healthy people and diabetes patients, as well as 387,076

Sparse learning methods have been powerful tools for learning compact representations of functional brain networks consisting of a set of brain network nodes and a connectivity matrix measuring functional coherence between the nodes. However, these tools typically focus on the functional connectivity measures alone, ignoring the brain network nodal information that is complementary to the functional

As a recently arisen framework, Label Distribution Learning (LDL) is one of the most appropriate machine learning paradigms to solve the label ambiguity problems. Due to the high cost, it is intractable to directly collect annotated distribution-level data. Therefore, Label Enhancement (LE) is proposed to obtain the label distribution for training LDL model by mining the information hidden in the logical