Cerebral microbleed (CMB) is a type of biomarker, which is related to cerebrovascular diseases. In this paper, a novel computer aided diagnosis method for CMB detection was presented. Firstly, sliding neighborhood algorithm was used to generate CMB and non-CMB samples from brain susceptibility weighted images. Then, a 15-layer proposed FeatureNet was trained for extracting features from the input samples

This paper proposes a modified marine predators algorithm based on quantum theory to handle the multilevel image segmentation problem. The main aims of using quantum theory is to enhance the ability of marine predators algorithm to find the optimal threshold levels to enhance the segmentation process. The proposed quantum marine predators algorithm gets the idea of finding a particle in the space based

This study presents a new fuzzy logic controller (FLC) for micro-electro-mechanical-system gyroscopes (MEMS-Gs). The nonlinearities and unknown dynamics are modeled by the designed non-singleton type-3 fuzzy system (NT3FS) and an adaptive control scheme is presented. To improve the control accuracy, the tracking error dynamics are identified by a deep learned Boltzmann machine (RBM) and the nonlinear

Most research on robot manipulation focuses on objects that are light enough for the robot to pick them up. However, in our daily life, some objects are too big or too heavy to be picked up or carried, so that dragging them is necessary. Although bipedal humanoid robots have nowadays good mobility on level ground, dragging unfamiliar objects including large and heavy objects on various surfaces is

To better handle problems with non-preferential multi-outputs (NPMO), a new approach is proposed in this study by employing the belief rule base (BRB) to provide a superior nonlinearity modeling ability as well as good explainability. The new approach is thus called NPMO-BRB. First, a new optimization model is constructed where the optimization objective is the integration of multi-outputs and respective

Sparse learning has significant applications in statistics, big data, bioinformatics and machine learning. In big data systems, a large amount of redundant, missing and noisy data cause sparsity, and the rapid changes of information result in uncertainty. Since the traditional sparse learning model is difficult to deal with uncertain data, we propose a Fuzzy Granular Sparse Learning (FGSL) model for

Electrical injury impacts are substantial and massive. Investments in electricity will continue to increase, leading to construction project complexities, which undoubtedly contribute to injuries and associated effects. Machine learning (ML) algorithms are used to quantify and model causes of injuries; however, conventional ML techniques do not produce optimal results since they require careful engineering

Measuring the similarity between images is of paramount importance in computer vision. However, the commonly used pixelwise similarity metrics do not match well with perceptual similarity. The purpose of this paper is to propose a visual similarity measurement method, which can be effectively used for plagiarism detection in graphic design. Plagiarism detection of designs refers to the identification

The workforce scheduling problem in call centres is defined as assigning a number of employees to various overlapping shifts during a specified planning horizon, considering some regulations and preferences, so as to minimize the labour cost. This is often difficult to solve to optimality due to its combinatorial structure. In this paper, we propose an enhanced artificial bee colony (EABC) algorithm

Today’s arena of global village organizations, social applications, and commercial websites provides huge information about products, individuals, and activities. This is leading to a plethora of content that requires effective handling to obtain the desired information. A recommendation system (RS) suggests relevant items to the user according to his/her desired preference. It processes various information

Choosing a good food supply chain is helpful for realizing the coordination of economic benefits and environmental optimization, so as to promote the sustainable development of the society. To solve such a problem, we need to ensure the accuracy of the original information and the validity of the aggregated information. However, experts may not be able to give professional evaluations in some aspects

In recent years, with the fast development of the internet and online platforms such as social media feeds, news blogs, and online newspapers, deceptive reports have been universally spread online. This manipulated news is a matter of concern due to its potential role in shaping public opinion. Therefore, the fast spread of fake news creates an urgent need for automatic systems to detect deceitful

Breast cancer is one of the deadliest forms of cancer in women but the disease has a good prognosis when diagnosed early. The gold standard for the diagnosis of breast cancer is mammography imaging analysis but the acquisition of mammograms is a painful and embarrassing procedure for women involving breast compression. Alternative methods have been investigated in the last years, including breast thermography

After months of lockdown due to the COVID-19 pandemic, more people are planning regional trips because overseas travel is still not feasible. However, choosing a suitable travel destination during the COVID-19 pandemic is challenging because the factors critical to the selection process are very different from those usually considered. Furthermore, without sufficient literature or data for reference

The prediction of the spread of coronavirus disease 2019 (COVID-19) is vital in taking preventive and control measures to reduce human health damage. The Grey Modeling (1,1) is a popular approach used to construct a predictive model with a small-sized data set. In this study, a hybrid model based on grey prediction and rolling mechanism optimized by particle swarm optimization algorithm (PSO) was applied

Over the past 20 years, the development of offshore wind farms has become increasingly important across the world. One of the most crucial reasons for that is offshore wind turbines have higher average speeds than those onshore, producing more electricity. In this study, a new hybrid approach integrating Interval Rough Numbers (IRNs) into Best-Worst Method (BWM) and Measurement of Alternatives and

When ambulances’ turnaround time (TT) in emergency departments is prolonged, it not only affects the victim severely but also causes unavailability of resources in emergency medical services (EMSs) and, consequently, leaves a locality unprotected. This problem may worsen with abnormal situations, e.g., the current coronavirus disease 2019 (COVID-19) pandemic. Taking this into consideration, this paper

Deciphering the signals that are attached to the transition from normal to disease stage is crucial in preventive medicine to understand the progression of complex diseases. Between normal and disease stages there exists the pre-disease stage, in which the disease is yet reversible towards the normal stage. Traditionally, molecular biomarkers have been used to identify the pre-disease stage. However

An accurate variable description of security returns is necessary to establish a valid portfolio optimization model. It is usually assumed to be a random variable when the historical data of security returns are sufficient. However, when historical data are too limited to estimate its probability distribution, uncertain variables are employed to characterize security returns to be effective. This paper

This study proposes novel integration of extreme learning machine (ELM) and adaptive neuro swarm intelligence (ANSI) techniques for the determination of California bearing ratio (CBR) of soils for the subgrade layers of railway tracks, a critical real-time problem of geotechnical engineering. Particle swarm optimization (PSO) with adaptive and time-varying acceleration coefficients (TAC) was employed

Label Distribution Learning (LDL) is a new learning paradigm with numerous applications in various domains. It is a generalization of both standard multiclass classification and multilabel classification. Instead of a binary value, in LDL, each label is assigned a real number which corresponds to a degree of membership of the object being classified to a given class. In this paper a new neural network

Despite the widespread application of deep neural networks in finance, medical treatment, and autonomous driving, these networks face multiple security threats, such as maliciously constructed adversarial samples that can easily mislead deep neural network model classification, causing errors. Therefore, creating an interpretable model or designing an interpretation method is necessary to improve its

As generalizations of single-valued information systems, interval-valued information systems (IVISs) can better express the real data with uncertainty in some applications. Attribute reduction methods for complete IVISs or complete interval-valued decision systems (IVDSs) have been developed. However, there are few researches on attribute reduction for incomplete interval-valued information systems

The purpose of this paper is to establish a new mathematical framework for knowledge measure (KM) from fuzzy sets (FSs) through interval-valued intuitionistic fuzzy sets (IVIFSs). A novel KM is developed first in the context of intuitionistic fuzzy sets (IFSs) based on the normalized Hamming distance combined with the technique for order preference by similarity to ideal solution (TOPSIS), complying

In this paper, we propose an online support vector quantile regression approach with an ε-insensitive pinball loss function, called Online-SVQR, for dynamic time series with heavy-tailed noise. Online-SVQR is robust to heavy-tailed noise, as it can control the negative influence of heavy-tailed noise by using a quantile parameter. By using an incremental learning algorithm to update the new samples

In this paper, allocation of hybrid photovoltaic panels, wind turbines and battery storage (PV/WT/BA) system in distribution network is presented aimed active losses cost minimization, voltage profile enhancement and minimizing power purchased from the hybrid system by the network. Meta-heuristic improved whale optimizer algorithm (IWOA) is used to determine the optimal location and size of the PV/WT/BA

Traditional Time Series Prediction (TSP) algorithms assume that the training and testing data follow the same distribution and a large amount of data can be obtained. However, the time-varying nature of time series data is a very difficult problem, which will lead to the inconsistent distribution between new data and old data. In TSP problem scenario, how to transfer knowledge in a relatively long-time

Wi-Fi fingerprint systems provide cost-effective and reliable solution for indoor positioning. However, such systems incur high calibration cost in the training phase and high searching overhead in the positioning phase. Moreover, huge storage requirement for the radio map of a large-scale fingerprint system is another major issue. Several solutions based on crowd-sourcing or machine learning technique

This paper proposes a maiden application of a two dimensional Sine Logistic map based chaotic sine cosine algorithm (2D-SLCSCA) optimized classical PID controller for load frequency control (LFC) of an islanded microgrid (MG). In comparison to random variables and 1D chaotic sequences, the 2D chaotic sequences are more ergodic and possess a wider chaotic range, thereby enhancing the global convergence

Anthropometry is a science concerning human body dimension measurements and proportions that is widely applied in multiple disciplines, particularly in apparel and “human-centered” product design. Designers and engineers generally use percentile anthropometric data that leads to significant errors in practice. Although adopting non-statistic body dimensions could resolve this issue, gaining detailed

Recursive neural networks are generally divided into dynamic neural networks and static neural networks to refer to the neural networks with one or more feedback links in the network structure. Inevitably, there exist some problems such as poor approximation performance and poor stable convergence performance due to complex network structure. The noise-tolerant gradient-oriented neurodynamic (NTGON)

Nowadays, most of the websites like Amazon, YouTube and Netflix use collaborative filtering methods to recommend various types of items to users. There are two principal categories of collaborative filtering; memory-based and model-based. The memory-based methods use the users’ similarity measures and have several advantages over the model-based techniques, including being easily explained and easy

Harris Hawks Optimizer (HHO) is one of the many recent algorithms in the field of metaheuristics. The HHO algorithm mimics the cooperative behavior of Harris Hawks and their foraging behavior in nature called surprise pounce. HHO benefits from a small number of controlling parameters setting, simplicity of implementation, and a high level of exploration and exploitation. To alleviate the drawbacks

Convolutional neural networks (CNNs) have been used to solve many problems in computer science with a high level of success, and have been applied in many fields in recent years. However, most of the designs of these models are still tuned manually; obtaining the highest performing CNN model is therefore very time-consuming, and is sometimes not achievable. Recently, researchers have started using

The paper analyzes the integrated fuzzy model with minimal latency in the location routing inventory of perishable multi-product materials, taking into account environmental constraints. Due to the corrupt nature of some items, minimizing the latency is very important in designing the supply chain systems of perishable items. On the other hand, considering environmental constraints provides a model

In this paper, three algorithms are applied to obtain the parameters of Radial Basis Function (RBF) kernels of Support Vector Machines (SVM), which include: PSO (Particle Swarm Optimization), GA (Genetic Algorithm) and GS (Grid Search). The three improved SVM approaches are applied to identify the risk of railway dangerous goods transportation system (RDGTS). The statistical occurrence frequency of

Because of the great achievements in designing remote sensing sensors, the extraction of useful information from multisource remote sensing data remains a challenging problem. Most of the recent research projects have applied single deep learning systems for data fusion and classification. The idea of using ensemble deep learning algorithms through a multisensor fusion system can improve the performance

Finding appropriate class-separating metric and labeling rules is crucial in the construction of image classifiers. In this paper a Divergence-Chebyshev Neighbors Pursuit (DCNP) algorithm is proposed for rapid Polarimetric Synthetic Aperture Radar (PolSAR) image classification. First, an information-theoretic divergence is defined to measure the similarity of polarimetric features between pixels. Then

In recent years, sustainable development and green manufacturing have attracted widespread attention to environmental problems becoming increasingly serious. Meanwhile, affected by the intensification of market competition and economic globalization, distributed manufacturing systems have become increasingly common. This paper addresses the energy-efficient scheduling of the distributed permutation

Over the last few years, the deep neural network is utilized to solve the collaborative filtering problem, a method of which has achieved immense success on computer vision, speech recognition as well as natural language processing. On one hand, the deep neural network can be used to capture the side information of users and items. On the other hand, it is also capable of modeling interactions between

Time-dependent reliability and sensitivity analysis, in which the nature of demand, capacity and the limit state function varies over the life cycle of the structural system, is a challenging task. Meta-models are a suitable tool to construct inexpensive-to-evaluate and accurate surrogates in analysis of modern engineering problems. The paper’s contribution is threefold: First, the superiority of polynomial

Online agricultural product trading has the characteristics of rapid and diversified transaction data; there is a fuzzy correspondence between sales volume influencing factors and sales volume levels. Based on this, this paper combines the data preprocessing technology of fuzzy membership and optimized deep learning algorithm, adding a self-encoding method with sparseness restriction, and proposes

This study proposes a new fuzzy adaptive Charged System Search (CSS) for global optimization. The suggested algorithm includes a parameter tuning process based on fuzzy logic with the aim of improving its performance. In this regard, four linguistic variables are defined which configures a fuzzy system for parameter identification of the standard CSS algorithm. This process provides a focus for the

IoMT sensors such as wearables, moodables, ingestible sensors and trackers have the potential to provide a proactive approach to healthcare. But grouping, traversing and selectively tapping the IoMT data traffic and its immediacy makes data management & decision analysis a pressing issue. Evidently, the selection process for real-world, time-constrained health problems involves looking at multivariate

To improve retrieval efficiency and quality, learning to hash has been widely used in approximate nearest neighbor queries. Deep learning is characterized by high precision in extracting data features; therefore, deep-learning-based hashing has attracted more attention. Existing methods have some weaknesses, such as complex training and losing spatial information. We design a new deep hashing algorithm

Industrial enterprises are the main body of national scientific and technological innovation activities, and the improvement of their research and development (R&D) output capacity plays a pivotal role in the enhancement of national innovation capabilities. The R&D process is an input–output process, and its results have various forms. In this study, we construct a multi-objective R&D investment allocation

Recent research has shown that the integration or blending of different forecasting models is able to improve the predictions of solar radiation. However, most works perform model blending to improve point forecasts, but the integration of forecasting models to improve probabilistic forecasting has not received much attention. In this work the estimation of prediction intervals for the integration

Adversarial pseudo-benign examples can be generated to evade malware detection algorithms based on deep learning. Current works on adversarial examples generation mainly focus on the gradient-based attacks due to their easy-to-implement features. Although the Generative Adversarial Network (GAN) has shown a superior performance on adversarial attacks, there is not much work on applying GAN to malware

Accurate train arrival delay prediction is critical for real-time train dispatching and for the improvement of the transportation service. This study proposes a data-driven method that combines eXtreme Gradient Boosting (XGBoost) and a Bayesian optimization (BO) algorithm to predict train arrival delays. First, eleven characteristics that may affect the train arrival time at the next scheduled station

Cooperation is a powerful strategy to reduce the costs of pickup and delivery services in the conventional two-echelon pickup and delivery problems (2E-PDPs). This study develops a cooperative 2E-PDP with state–space–time network (C2E-PDPSST) and incorporates vehicle routing problem and profit allocation optimization. Transportation resource sharing (trucks/vehicles) is presented as a major cooperative