Many methods based on matrix factorization have recently been proposed and achieve good performance in many practical applications. Latent low-rank representation (LatLRR) is a marvelous feature extraction method, and it has shown a powerful ability in extracting robust data features. However, LatLRR and the variants of LRR have some shortcomings as follows: (1) The label information of the original

Owing to today’s highly competitive market environments, substantial attention has been focused on sustainably resilient supply chains (SCs) over the last few years. Nevertheless, very few studies have focused on the efficiency evaluation analysis of the sustainability and resilience of SCs as an inevitable essential in any profitable business. This study aims to address this issue by proposing a novel

Air temperature prediction is a significant task for researchers and forecasters in the field of meteorology. In this paper, we present an innovative, deep spatial–temporal learning air temperature forecasting framework based on graph attention network and gated recurrent unit. Particularly, historical observations containing multiple environmental variables at different stations are constructed as

We propose Fuzzy Jaccard Index (Fuji) — a scale-invariant score for similarity assessment of two ranked/ordered lists. Fuji improves upon the Jaccard index by incorporating a membership function that takes into account the particular ranks, thus producing both more stable and more accurate similarity estimates. We provide theoretical insights into the properties of the Fuji score as well as propose

In this paper, MASA (Modified A* Algorithm) method is introduced which can merge numerous factors with different weights into a multi-weighted graph to offer the most optimal route for a pedestrian tourist. Several mobile applications support personalized tour recommendations in making feasible plans, however, tours should not only make visiting attractions possible via the shorter route but also satisfy

The widespread usages of sensitive equipment such as computers, controllers and microelectronic devices have placed immense burden on the grid operators to deliver high quality electrical power to their customers. To achieve this end, the power quality disturbances (PQD) within the network need to be minimized. In this paper, a method to enhance the performance of the multiclass support vector machine

Various crop diseases are a major problem worldwide since their occurrence leads to a significant decrease in crop production. The image-based automatic identification of crop diseases that involves food security has attracted much attention recently. It is a challenging research topic due to the complexity of crop disease images, such as clutter field backdrops and irregular lighting strengths. A

Aiming at the shortcomings of traditional genetic algorithm (GA) in the overall evolution of hardware, such as slow speed and small scale, a method of evolvable hardware (EHW) based on elite Partheno-Genetic Algorithm (PGA) is proposed. To improve the evolution efficiency of EHW, the number of chromosomes is reduced, the length of the chromosome is increased, the elite node chain is reserved, and the

With the development of power systems, fault diagnosis in infrared images has become increasingly important for ensuring the stability of these systems. In this paper, we propose a multi-objective multilevel threshold image segmentation method based on the boost marine predators algorithm (BMPA) for infrared-image fault diagnosis. As fault points are small, it is difficult to detect the target in an

Credit scoring is playing an increasingly critical role with the rising number of lending operations for micro and small enterprises as well as individuals. A large number of research is primarily based on the combination and construction methods of credit scoring models by the experts. However, different credit data have distinct requirements for the models, and how to automatically search and construct

As a potential new energy power generation technology, wind power is gradually developing into the world’s mainstream energy. In the research on wind power generation, wind speed prediction is an important part, which has been widely studied. The accurate wind speed prediction is a key part of wind power management to help wind power grid-tied. Currently, most research has focused on point prediction

Improve the reliability of wind speed forecasting is a crucial task in wind power generation. Due to the stochastic and noise nature of wind, a preprocessing step is beneficial for wind speed series to get clean data. The decomposition technique is reported as the critical preprocessor to transform the unstable wind speed data into several regular components. This study proposes a hybrid forecasting

Recently, many dynamic optimization algorithms based on metaheuristic methods have been proposed. Although these schemes are highly efficient in determining a single global optimum, they fail in locating multiple optimal solutions. The central goal of dynamic multimodal optimization is to detect multiple optimal solutions for an optimization problem where its objective function is modified over time

Inter-release defect prediction (IRDP) is a practical scenario that employs the datasets of the previous release to build a prediction model and predicts defects for the current release within the same software project. A practical software project experiences several releases where data of each release appears in the form of chunks that arrive in temporal order. The evolving data of each release introduces

The widespread development of conversational agents (chatbots) has enabled us to communicate and collaborate with different forms and functions of robots using natural language, thus facilitating a closer relationship between humans and technology. Given that chatbot services infused with domain knowledge are of great interest to not only global businesses but also academics, chatbots have in recent

In engineering optimization, surrogate model (SM) is widely used to replace the involved time expensive model, due to the expensive model is complex and high precise requirement caused a long calculation cycle. In traditional process of engineering optimization, the separation of the surrogate model static construction stage and dynamic optimization stage depresses the optimization accuracy and efficiency

Copy-move forgery is one of the well-known image forgery technique which exploits regions of the same image to create forged image by replicating or hiding authentic content of the original image. Original images can also contain similar looking but authentic objects. In such cases, identification of authentic and tampered images is a complicated task. To tackle this problem, we propose a method in

In this paper, we consider an NP-hard, real-world optimization problem from the field of computer networks. The problem refers to the network survivability and may be considered hard due to its scale. Such problems are usually successfully solved by various Genetic Algorithms (GAs). The recent advances in the development of Evolutionary Algorithms (EAs) dedicated to solving discrete-encoded problems

This paper proposes new methods of computing 2D bifurcation diagrams for nonlinear time series using MultiLayer Perceptrons (MLPs), LSTM Fully Convolutional Networks (LSTM-FCN), Time Series Forests (TSFs) with entropy, Gini impurity, and K-Nearest Neighbors (KNNs) algorithm with Dynamic Time Warping (DTW). The proposed algorithms can precisely compute 2D bifurcation diagrams for oscillatory time-series

Imbalanced problem is concerned with the performance of classifiers on the data set with severe class imbalance distribution. Traditional methods are misled by the majority samples to make the incorrect prediction and fail to make full use of minority samples. This paper is motivated to design a novel hybrid ensemble learning strategy named Sample and Feature Selection Hybrid Ensemble Learning (SFSHEL)

Object detection from satellite images is challenging and either computationally expensive or labor intense. Satellite images often cover large areas of more than 10km×10km. They include objects of different scales, which makes it hard to detect all of them at the same image resolution. Considering that airplanes are usually located in airports, ships are often distributed in ports and sea areas, and

In enterprises, machines are subject to some unavailable periods resulting in production halts. The consideration of preventive maintenance (PM) and resource assignment in straight and U-shaped assembly lines is of paramount importance. Hence, this paper aims to tackle the integration of PM and balancing problems in straight and U-shaped resource-dependent assembly lines via two new mixed-integer linear

In this study, we propose hierarchical polynomial-based fuzzy neural networks (HPFNN). The aim of this study is to develop the design methodologies of hierarchical model to improve the prediction accuracy of the model without sacrificing computational efficiency through combining hybrid network architecture composed of different neurons and ranking-based neuron selection strategies. The essential bullets

Several dimensionality reduction techniques were applied to hyperspectral reflectance images of wine grape berries, leading a study of the machine learning models’ efficiency in the prediction of sugar content for training, validation and independent test sets, and for generalization sets with vintages not previously seen in the training phase. The results obtained across all settings were up to par

The current study proposes a new metaheuristic algorithm based on the clan behavior of elephants, called elephant clan optimization (ECO), to solve structural optimization problems. This method is a new version of the previously developed algorithm; namely, the elephant herding optimization (EHO). While the EHO algorithm has been inspired by the behavior of elephants, the theory behind this method

The new form of artificial intelligence training, i.e. federated learning, is becoming more popular in the last few years. It is an optimization problem that includes additional mechanisms such as aggregation and data transmission. In this paper, we propose a hybridization of this type of training with a meta-heuristic. The meta-heuristic algorithm is adapted to manage the entire process as well as

A neighborhood information system (NIS) deals with an information system (IS) by means of neighborhoods. Sometimes it has some advantages over an IS. A neighborhood decision information system (NDIS) means a NIS with decision attributes. Single cell RNA (scRNA) data possess the characteristics of high dimensionality, small sample, unbalanced distribution, big noise and high redundancy. It has become

Smart Cities contains millions of IoT sensors supporting critical applications such as Smart Transport, Buildings, Intelligent Vehicles, and Logistics. A central administrator appointed by the government manages and maintains the security of each node. Smart City relies upon millions of sensors that are heterogeneous and do not support standard security architecture. Different manufacturers have weak

There is a very extensive literature on the design and test of models of environmental pollution, especially in the atmosphere. Current and recent models, however, are focused on explaining the causes and their temporal relationships, but do not explore, in full detail, the performances of pure forecasting models. We consider here three years of data that contain hourly nitrogen oxides concentrations

In order to improve integrity management evaluation level of pipeline, the fuzzy surfacelet neural network optimized by improved dragonfly algorithm. Firstly, the domestic and foreign related research progresses of pipeline integrity management are summarized. Secondly, the pipeline integrity management evaluation index system is constructed according to management and technology characteristics of

Despite the initial hype surrounding blockchain applications in the supply chain, real-world implementation of this disruptive technology faces significant challenges. To manage these challenges and ensure a smooth implementation, supply chain organizations must perform various activities to acquire readiness. This paper proposes a readiness assessment and management approach for blockchain implementation

Deep Convolutional Neural Networks are able to identify complex patterns and perform tasks with super-human capabilities. However, besides the exceptional results, they are not completely understood and it is still impractical to hand-engineer similar solutions. In this work, an image classification Convolutional Neural Network and its building blocks are described from a frequency domain perspective

Compute first networking (CFN) is a latest distributed framework that intelligently allocates computing resources for edge computing according to computing load and network status. It requires real-time visibility of available statuses of local or remote computing resources. To the best of our knowledge, this paper is the first to propose a centralized fault-detection protocol called CFN-Watchdog to

With the increasing awareness of environmental protection, green supplier selection as an indispensable part of green supply chain management has received extensive attention. Effective and reliable green supplier evaluation criteria are crucial to the success of green supplier selection. Therefore, identifying the critical criteria and determining the causality of these criteria is an important requirement

The purpose of feature selection (FS) is to eliminate redundant and irrelevant features and leave useful features for classification, which can not only reduce the cost of classification, but also improve the classification accuracy. Existing algorithms mainly focus on finding one best feature subset for an optimization target or some Pareto solutions that best fit multiple targets, neglecting the

In recent times,COVID-19, has a great impact on the healthcare sector and results in a wide range of respiratory illnesses. It is a type of Ribonucleic acid (RNA) virus, which affects humans as well as animals. Though several artificial intelligence-based COVID-19 diagnosis models have been presented in the literature, most of the works have not focused on the hyperparameter tuning process. Therefore

Road target detection is a very challenging task in the field of computer vision because it is easily affected by complex backgrounds and sparse features of small targets. YOLOv3 (You Only Look Once v3) is currently one of the state-of-the-art object detection methods of deep learning. However, because the k-means clustering algorithm is sensitive to the initial clustering center, the local fragile

In this paper, a multilingual end-to-end framework, called ATCSpeechNet, is proposed to tackle the issue of translating communication speech into human-readable text in air traffic control (ATC) systems. In the proposed framework, we focus on integrating multilingual automatic speech recognition (ASR) into one model, in which an end-to-end paradigm is developed to convert speech waveforms into text

The metaheuristic algorithms do not depend on the functional form when solving the optimization problem. They have strong adaptability and are widely used in many fields. Whale Optimization Algorithm (WOA) is a metaheuristic algorithm based on the social behavior of humpback whales. Compared with other metaheuristic algorithms, WOA shows better performance. However, when solving high dimensional optimization

Semisupervised learning (SSL) models are useful for undertaking classification problems with a small set of labeled samples and a large number of unlabeled samples. In this regard, the family of fuzzy min–max (FMM) neural networks offers the capability of online learning for addressing both unsupervised and supervised problems. As such, this paper proposes a novel two-stage SSL model based on FMM networks

Kernel ridge regression (KRR) is a widely accepted efficient machine learning paradigm that has been fruitfully implemented for solving both classification and regression problems. KRR solves a set of linear equations instead of solving a quadratic programming problem. However, KRR gives equal importance to each sample which leads to giving the same significance to the important and non-important samples

Combining the predictions of different base experts is a well known approach used to improve the accuracy of time series forecasts. Forecast aggregation is becoming crucial in many fields, including electricity forecasting, as Internet of Things and Cloud technology give access to larger numbers of sensor data, time series and predictions from external providers. In this context, it is not uncommon

Differential evolution and its variants have already proven their worth in the field of evolutionary optimization techniques. This study further enhances the success history-based adaptive differential evolution (SHADE) by hybridizing it with a modified Whale optimization algorithm (WOA). In the new algorithm, the two algorithms, SHADE and modified WOA, carry out the search process independently and

Unwanted errors caused by hand tremors are a bottleneck for the application of teleoperation robots in space explorations, underwater explorations, and minimally invasive surgery. In order to eliminate hand tremor signals in teleoperation control systems, two tremor-filtering models based on artificial neural networks are defined. With the purpose of decreasing the errors of tremor filtering models

Data quantity generated in supply chains expands as supply chain processes get more complex. Companies can leverage such data and gain valuable insight into their processes with Supply Chain Analytics (SCA) tools. The selection of the most appropriate SCA tool directly affects companies’ productivity since they allow enhancing visibility, obtaining informed decision-making and developing well-planned

The printed circuit boards (PCBs) grouping problem (PGP) is an essential part of PCBs assembly that has attracted much attention in recent years. In this paper, we propose a mathematical model and an iterated greedy (IG) algorithm, called IGP, for solving the PGP with setup time criterion. Based on the problem characteristics, two speed-up theorems are proposed and applied in the IGP. In the IGP, a

Random Vector Functional Link (RVFL) is popular among researchers in many areas of machine learning. RVFL is preferred by many researchers as RVFL can produce good performance with relatively little training time. Recent works extend RVFL into deep and ensemble versions. However, RVFL does not have effective feature extraction methods commonly used in time series classification. This results in poor

In smart factories, the data collected by Internet-of-things sensors is enormous and includes a lot of noise and missing values. To address this big data problem, metaheuristic is one of the main approaches to data preprocessing, i.e., instance selection or feature selection before training the model. Most previous works on metaheuristic approaches rarely considered simultaneous instance selection

Community detection in social networks is becoming one of the key tasks in social network analysis, since it helps analyzing groups of users with similar interests. This task is also useful in different areas, such as biology (interactions of genes and proteins), psychology (diagnostic criteria), or criminology (fraud detection). This paper presents a metaheuristic approach based on Variable Neighborhood

This paper proposes the definition of the architecture of an evolving fuzzy neural network based on self-organizing direction aware data partitioning through stochastic processes based on the dataset used in the model. The choice between four different types of logical neurons in the second layer and the six different types of neuron activation function that compose the artificial neural network of

This paper considers open-domain and multi-hop reading comprehension tasks that require complex multi-step reasoning processes. The study is particularly challenging because it requires a model to learn to explore “bridge” information to connect text snippets relevant to the answer. Unlike the usual neural-network-based retrieval models, which are difficult to interpret, this paper proposes a coarse-to-fine

Large datasets are necessary for deep learning as the performance of the algorithms used increases as the size of the dataset increases. Poor data management practices and the low level of digitisation of the construction industry represent a big hurdle to compiling big datasets; which in many cases can be prohibitively expensive. In other fields, such as computer vision, data augmentation techniques

Optimizing the reliability and the robustness of a design is important but often unaffordable due to high sample requirements. Surrogate models based on statistical and machine learning methods are used to increase the sample efficiency. However, for higher dimensional or multi-modal systems, surrogate models may also require a large amount of samples to achieve good results. We propose a sequential