In recent years, automated whole breast ultrasound (ABUS) has drawn attention to breast disease detection and diagnosis applications. However, reviewing ABUS volumes is a time-costing task and some subtle tumors may be missed. In this paper, a 3D multi-view tumor detection method is proposed for ABUS volumes. Firstly, a layer connected feature extraction network is designed for Faster R-CNN. Then,

Parkinson’s disease (PD) is commonly characterized by several motor symptoms, such as bradykinesia, akinesia, rigidity, and tremor. The analysis of patients’ fine motor control, particularly handwriting, is a powerful tool to support PD assessment. Over the years, various dynamic attributes of handwriting, such as pen pressure, stroke speed, in-air time, etc., which can be captured with the help of

The multi-objective quality of service (QoS)-driven web service composition problem (MOQWSCP) aims to find the best combinations of atomic web services (i.e. composite service) to answer high quality of the optimized QoS criteria in a way that maximize benefit QoS parameters such as availability and reliability and minimize the negative ones like price and response time, where the users’ requirements

Feature based learning of time series sequences contains a systematic step of preprocessing, representing and analyzing the properties of time series elements. Representational features include the mapping of time series properties namely trend, seasonality and stationarity. Usually, the segmented generation of representational structures does not contain the global features of a time series sequence

Owing to the nondeterministic nature of wind speed, the conventional fuzzy time series forecasting model has difficulty in establishing a common membership level. Therefore, in this study, the fuzzy series forecasting model was improved based on hesitant fuzzy sets. A hesitant fuzzy wind speed forecasting system with a novel defuzzification method and multiobjective optimization algorithm was developed

The concept of measuring the membership degree along with a non-membership degree, gives rise to the intuitionistic fuzzy set theory. The present study formulates a nonlinear programming problem with multiple objectives, including, all the parameters and decision variables as intuitionistic fuzzy numbers. The problem is further investigated subject to optimistic, pessimistic and mixed approaches under

This paper proposes a two-stage multi-objective possibilistic integer linear programming sustainable supply chain network design model, minimizing the economic, environmental goals and maximizing the social sustainability goals. The proposed model determines the openings of facilities and the amount of flow of goods across the supply chain. It introduces supplier green image factors in the design of

Facility location problems (FLP) are often solved as uncapacitated facility location (UFL) instances. Also, typical solution approaches in the literature assume that the established facilities are totally reliable. However, in practice, facilities have limited capacity and can be under risk of partial disruptions whereby their failure leads to a notably higher cost. In this context, this paper presents

Current advances in driver drowsiness detection consist of a variety of innovative technologies generally based on driver state monitoring systems. Extracting effective and relevant features to characterize drowsy symptoms in images and videos is still an open topic. In this work, we introduce a face monitoring system based on a compact face texture descriptor able to cover the most discriminant drowsy

Salp swarm algorithm (SSA) is a recently presented algorithm, which is simple in structure and relatively first-rate in its performance. However, the original SSA still has features to be improved because it may face problems in convergence trends or easily being trapped into local optima for more advanced problems. To alleviate this limitation, we propose a new SSA-based method (MCSSA) that performs

Tumor segmentation is a crucial but difficult task in treatment planning and follow-up of cancerous patients. The challenge of automating the tumor segmentation has recently received a lot of attention, but the potential of utilizing hybrid positron emission tomography (PET)/magnetic resonance imaging (MRI), a novel and promising imaging modality in oncology, is still under-explored. Recent approaches

Large-scale group decision-making process has received an increasing attention in recent years. After making the general survey of the existing large-scale group decision-making methods, we have found that: 1) consistency threshold value of hesitant fuzzy linguistic preference relation is fixed in traditional consistency measures; 2) the clustering process of LSGDM does not consider the similar relationship

Many real-world applications generate attributed graphs that contain both link structures and content information associated with nodes. Content information in real networks always contains high dimensional feature space. In recent years, unsupervised feature selection has been widely used in handling high dimensional data without label information. Most existing unsupervised feature selection methods

Recently, the conditional random field (CRF) model has been greatly considered in synthetic aperture radar (SAR) image segmentation. This model not only directly considers the posterior distribution of the label field conditioned on images but also gives the interactions between the observations. In this paper, we propose a new CRF-based algorithm for SAR image segmentation. We consider the statistical

Cloud services have become a popular and flexible solution for providing components to build service-based systems. A component’s trustworthiness is a key measure that can guide service requesters when making a service selection decision. Prediction of this trustworthiness, based on the component’s multi-faceted quality of service (QoS) attributes, is therefore an important problem to address. In this

The main objective of this study is to first propose a novel integrated framework of fuzzy risk assessment model (FRAM), data envelopment analysis (DEA), and multiple criteria decision-making (MCDM) approaches for route selection in multimodal transportation networks. In the FRAM phase, the magnitude calculation of risks was operated by decision makers, who can provide their opinions on the probability

Cross Site Scripting (XSS) is one of the most frequently occurring vulnerability. The impact of XSS can vary from cosmetic to catastrophic damages. However, detection of XSS efficiently is still an open issue. Cross site scripting has been dealt with static and dynamic analysis previously. Both techniques have shortcomings and fail due to frequent variations in XSS payloads. Therefore, in this paper

For a target task where the labeled data are unavailable, unsupervised domain adaptive learning performs transfer learning from labeled source data to unlabeled target data. Previous deep domain adaption methods mainly learned the global domain shift between different domains, the global distributions are aligned without considering the correspondence information between the same class data of different

Blockchain, the underlying technology of Bitcoin, refers to the public ledger used in a distributed network. Because blockchain does not rely on a central authority, peers have to agree on the state of the ledger among themselves, i.e., they have to reach a consensus on the state of the transactions. The way nodes reach that consensus has gained incredible attention in the literature. Bitcoin uses

An area of consumer behaviour that caught retailers and supply chains unprepared during the initial stages of the COVID-19 pandemic was the increased prevalence of the purchase of utilitarian goods – referred to in the media as “panic buying.” In this study, we take a novel approach to understanding such panic buying during the pandemic using compensatory control theory (CCT), text analytics, and advanced

Active learning methods increase classification quality by means of user feedback. An important subcategory is active learning for outlier detection with one-class classifiers. While various methods in this category exist, selecting one for a given application scenario is difficult. This is because existing methods rely on different assumptions, have different objectives, and often are tailored to

Collaborative filtering (CF) recommendation is well-known for its outstanding recommendation performance, but previous researches showed that it could cause privacy leakage for users due to k-nearest neighboring (KNN) attacks. Recently, the notion of differential privacy (DP) has been applied to privacy preservation in recommendation systems. However, as far as we know, existing differentially private

Salp swarm algorithm (SSA) based on the swarming behaviour of salps found in ocean, is a very competitive algorithm and has proved its worth as an excellent problem optimizer. Though SSA is a very challenging algorithm but it suffers from the problem of poor exploitation, local optima stagnation and unbalanced exploration and exploitation operations. Thus in order to mitigate these problems and improve

The statistical modeling of mobile radio signals requires the estimation of parameters that describe the probability distribution that hypothetically models this channel, so that this probabilistic model guarantees a good adjustment to the experimental data. This article proposes the use of differential evolution (DE) algorithms for estimating parameters of the α−η−μ fading channel, and to compare

Peripheral neuropathy that interrupts sensorimotor integration in the motor control process will lead to hand and upper limb motor function deficits in daily life. However, behavioral biomechanics and motor functions have never been considered in available diagnoses and clinical evaluations. Previous studies that investigate the behavioral biomechanics to delineate a specific peripheral neuropathy

Word sense disambiguation is a process to correctly identify the meanings of words in a given context. Being important in many natural language processing applications, this process is crucial in automatically understanding natural language expressions. Herein, we propose a variation of a well-known unsupervised graph-based word sense disambiguation method that utilizes all possible semantic information

A Probabilistic Physics-guided Neural Network (PPgNN) is proposed in this paper for probabilistic fatigue S-N curve estimation. The proposed model overcomes the limitations in existing parametric regression models and classical machine learning models for fatigue data analysis. Compared with explicit regression-type models (such as power law fitting), the PPgNN is flexible and does not impose restrictions

Numerous number of tourism attractions along with a huge amount of information about them on web and social platforms have made the decision-making process for selecting and visiting them complicated. In this regard, the tourism recommendation systems have become interesting for tourists, but challenging for designers because they should be able to provide personalized services. This paper introduces

Nonlinearity and high volatility of financial time series have made it difficult to predict stock price. However, thanks to recent developments in deep learning and methods such as long short-term memory (LSTM) and convolutional neural network (CNN) models, significant improvements have been obtained in the analysis of this type of data. Further, Empirical Mode Decomposition (EMD) and Complete Ensemble

There is growing interest in the use of automated psychological profiling systems, specifically applying machine learning to the field of deception detection. Several psychological studies and machine-based models have been reporting the use of eye interaction, gaze and facial movements as important clues to deception detection. However, the identification of very specific and distinctive features

Time-interval-related pattern (TIRP) mining algorithms find patterns such as “A starts B” or “A overlaps B”. The discovery of TIRPs is computationally highly demanding. In this work, we introduce a new efficient algorithm for mining TIRPs, called vertTIRP which combines an efficient representation of these patterns, using their temporal transitivity properties to manage them, with a pairing strategy

A clinical treatment process typically carries out in two stages; i.e., hospital stay and treatment at home after hospitalization. The correct completion of the treatment process is essential, but it becomes challenging for elders and patients with any physical or cognitive disability since they need assistance in the execution of the treatment itself. This work presents an intelligent system able

With the rapid development of the internet, more and more sophisticated data can be utilized by recommendation systems to improve their performance. Such data consist of heterogeneous information networks (HINs) made up of multiple nodes and link types. A critical challenge is how to effectively extract and apply the useful HIN information. In particular, the embedding-based recommendation approach

Breast calcifications in radiographic images suggest a high likelihood of breast lesion malignancy. However, it is difficult to detect calcifications in traditional B-mode ultrasound images due to resolution limits and speckle noise. In this paper, we propose a unified deep learning framework for automatic calcification detection based on multichannel ultrasound radio frequency (RF) signals. First

This paper presents an algorithmic method that hybridizes solution procedures with an optimization model for solving the problem of scheduling and allocating changeover tasks. This problem involves a limited number of changeover workers with different skills and a set of sequence-dependent changeover tasks on a multi-machines production line, where executing each task requires a particular type and

Sentiment Analysis is a process that aids in assessing the performance of products or services from user generated online posts. In present time, there are various websites that allow customers to post reviews about movies, products, events or services, etc. This has led to cumulative aggregation of a lot of reviews written in natural language. Prevailing factors such as availability of online reviews

K-means is a well-known unsupervised-learning algorithm. It assigns data points to k clusters, the centers of which are termed centroids. However, these centroids have a structure usually represented by a list of quantized vectors, so that kernel density estimation models can better represent complex data distributions. This paper proposes a k-means-based supervised-learning clustering method termed

Meta evaluation theory and methods were used to evaluate the review experts of science and technology projects. Dozens of meta evaluation criteria can be found within two categories: the objective data of the experts' review results, i.e. the coefficient of deviation, the Spearman rank correlation coefficient, the reliability coefficient of binary value; the subjective data of the experts, i.e., the

Gaussian kernel is defined by Euclidean distance and has been widely used in many fields. In the view of Euclidean distance is sensitive to outliers or noise and it is difficult to obtain satisfactory results for complex non-convex data. Entropy-like divergence is firstly induced by combining Jenson-Shannon/Bregman divergence with convex function, and its mercer kernel function called entropy-like

The paper presents a novel method of finding a fragment in a long temporal sequence similar to the set of shorter sequences. We are the first to propose an algorithm for such a search that does not rely on computing the average sequence from query examples. Instead, we use query examples as is, utilizing all of them simultaneously. The introduced method based on the Dynamic Time Warping (DTW) technique

Lung modules are an initial indicator as to whether or not a patient will develop lung carcinoma which has a significant mortality rate if it is not detected at an early stage.The detection of these lung nodules is a complex task and is time-consuming for the radiologist. For these reasons, CAD systems have been developed and employed for the detection of lung nodules. In this article, we present a

Social media plays an important role in facilitating conversations and news dissemination. Specifically, Twitter has recently seen use by investors to facilitate discussions surrounding stock exchange-listed companies. Investors depend on timely, credible information being made available in order to make well-informed investment decisions, with credibility being defined as the believability of information

The worldwide adoption of mobile devices is raising the value of Mobile Performance Marketing, which is supported by Demand-Side Platforms (DSP) that match mobile users to advertisements. In these markets, monetary compensation only occurs when there is a user conversion. Thus, a key DSP issue is the design of a data-driven model to predict user conversion. To handle this nontrivial task, we propose

Peer review is an essential part of scientific communications to ensure the quality of publications and a healthy scientific evaluation process. Assigning appropriate reviewers poses a great challenge for program chairs and journal editors for many reasons, including relevance, fair judgment, no conflict of interest, and qualified reviewers in terms of scientific impact. With a steady increase in the

Two-sided matching decision making (TSMDM) problems exist widely in human being’s daily life. For practical TSMDM problems, matching objects with different culture and knowledge backgrounds usually tend to provide linguistic assessments using different linguistic term sets (i.e., multi-granular linguistic information). Moreover, for TSMDM problems with high uncertainty, it is possible that matching

When solving group decision-making (GDM) problems, the proper values of consensus thresholds are vital for consensus checking and improving. Decision makers are usually not confident to select a proper threshold. In the linguistic setting, this paper presents a new process to support the determination of consensus thresholds. A new concept, namely random consensus index, is presented to serve as a

This paper presents a novel architecture for dealing with Automatic Fake News detection. The architecture factors in the discourse structure of news in traditional digital media and is based on two premises. First, fake news tends to mix true and false information with the purpose of confusing readers. Second, this research is focused on fake news delivered in traditional digital media, so our approach

Given a social network where the users are associated with non-uniform selection cost, the problem of Budgeted Influence Maximization (BIM in short) asks for selecting a subset of the nodes within an allocated budget for initial activation, such that due to the cascading effect, influence in the network is maximized. In this paper, we study this problem with a variation, where a subset of the users

This paper proposes new versions of Harris Hawks Optimizer (HHO) incorporated the survival-of-the-fittest principle of evolutionary algorithms. HHO is the recent swarm-based optimization algorithm imitating the surprise pounce behaviour of Harris’ hawks chasing style. HHO can show different patterns of the exploration and exploitation. It has a simple and time-varying structure, which further assist

Nonconvex problems have recently received considerable attention in statistics, machine learning and vision areas. Variance reduction techniques like the SVRG-type and SARAH-type methods, originally proposed for convex optimization, have been proved to be effective for nonconvex optimization. The performance of variance-reduced stochastic gradient methods greatly depends on how step sizes are tuned

The propositionalization process tries to find distinctive features of the examples in a database to transform such relational data into a simpler representation. More informative features have a positive impact on the classification capabilities of the learning algorithms. In this work, we propose a new propositionalization method, which generates complex Boolean attributes using Grammar-Guided Genetic

This paper proposes a new hybrid metaheuristic algorithm that is composed of the adaptive large neighbourhood search (ALNS) and the variable neighbourhood search (VNS) algorithms to tackle the location routing problem (LRP) with capacity constraints. The rationale of the proposed hybrid metaheuristic algorithm is to enhance the performance of the ALNS algorithm by incorporating the VNS algorithm as

The amount of available multimedia data in different formats and from different sources increases everyday. From an information retrieval point of view, this high volume and heterogeneity of data involves several issues to be addressed related to information overload and lacks of well structured information. Even if modern information retrieval systems offer to the user manifold search options, it

This study focuses on horizontal collaboration and addresses a collaborative multi-center vehicle routing problem (CMCVRP) deriving from the unmanned micro warehouse pattern in new retail in China. In the CMCVRP, suppliers form a coalition to reduce their operating costs and improve service levels. Traditional approaches cannot coordinate the interests of all decision subjects in this process. To fill

Online reviews have a substantial impact on decision making in various areas of society, predominantly in the arena of buying and selling of goods. As such, the truthfulness of internet reviews is critical for both consumers and vendors. Fake reviews not only mislead innocent clients and influence customers' choice, leading to inaccurate descriptions and sales. This raises the need for efficient fake

This paper explores the impact of the development indicators of Brazilian municipalities in the results of the 2018 Brazilian presidential elections. As the development indicator of a municipality we used the Municipal Human Development Index (MHDI), a known index derived from the Human Development Index (HDI) that has been adapted to Brazilian reality. The MHDI is composed by three sub-indices: education

Automated classification of Alzheimer’s disease (AD) plays a key role in the diagnosis of dementia. In this paper, we solve for the first time a direct four-class classification problem, namely, AD, Normal Control (CN), Early Mild Cognitive Impairment (EMCI) and Late Mild Cognitive Impairment (LMCI) by processing Diffusion Tensor Imaging (DTI) in 3D. DTI provides information on brain anatomy in form

Microblog sentiment analysis has become a hot research area due to its wide applications. There are some methods utilizing social context, but they only built a global sentiment analysis model, failing to extract personalized expressions. Some personalized methods have been proposed to deal with this problem, but they suffer from data sparseness and inefficiency. Based on personalized sentiment analysis

Weight-based feature selection (WBFS) are methods used to measure the contribution of input to output in a trained artificial neural network (ANN). Furthermore, algorithms such as Garson’s rely upon a single best neural network model or the mean importance value of several ANNs. However, different initialization weights lead to different importance values, as reported in other studies. These differences