Memory-based collaborative filtering is one of the recommendation system methods used to predict a user’s rating or preference by exploring historic ratings, but without incorporating any content information about users or items. It can be either item-based or user-based. Taking item-based Collaborative Filtering (CF) as an example, the way it makes predictions is accomplished in 2 steps: first, it

Outlier identification is an important technology to improve the credibility of data and aims at detecting patterns that rarely appear and exhibit a significant difference from other data. However, the detection accuracy achieved by the simple deviation factors of existing pattern-based outlier detection methods is not competitive. In addition, given the large scale of uncertain data streams, the efficiency

Over the last decade, Unmanned Aerial Vehicles (UAVs) have been extensively used in many commercial applications due to their manageability and risk avoidance. One of the main problems considered is the Mission Planning for multiple UAVs, where a solution plan must be found satisfying the different constraints of the problem. This problem has multiple variables that must be optimized simultaneously

Due to the rapid growth of web services in repositories, discovering the requisite web service is becoming increasingly cumbersome task. It has raised the demand for efficient web service clustering algorithms. In service repositories, when related web services are stored in a clustered way, it enhances the web service discovery process by reducing search space and time. Many eminent researchers have

The paper addresses the question of ensuring comparability in data envelopment analysis (DEA) in situations when units are organized in an ordered hierarchy with functions shared at different levels. In such a case, although units may have identical input-output sets that they put to use in similar production, they are not an ideally homogeneous group and their comparability in a benchmarking context

Content-based medical image retrieval (CBMIR) is one of the most challenging and ambiguous tasks used to minimize the semantic gap between images and human queries in datasets with rich information content. Similar to the human visual saliency mechanism, CBMIR systems also use the visual features in the images for searching purposes. As a result of this search process, automatically accessing the images

A new optimization method namely the Search and Rescue optimization algorithm (SAR) is presented here to solve constrained engineering optimization problems. This metaheuristic algorithm imitates the explorations behavior of humans during search and rescue operations. The ε-constrained method is utilized as a constraint-handling technique. Besides, a restart strategy is proposed to avoid local infeasible

The problem of automatically measuring the degree of semantic similarity between textual expressions is a challenge that consists of calculating the degree of likeness between two text fragments that have none or few features in common according to human judgment. In recent times, several machine learning methods have been able to establish a new state-of-the-art regarding the accuracy, but none or

A major challenge in automating the production of a large number of forecasts, as often required in many business applications, is the need for robust and reliable predictions. Increased noise, outliers and structural changes in the series, all too common in practice, can severely affect the quality of forecasting. We investigate ways to increase the reliability of exponential smoothing forecasts,

Stock returns are continuously generated by different data sources and depend on various factors such as financial policies and national economic growths. Stock returns prediction, unlike traditional regression, requires consideration of both the sequential and interdependent nature of financial time-series. This work uses a two-stage approach, using kernel adaptive filtering (KAF) within a stock market

Non-invasive load monitoring (NILM) is a vital step to realize the smart grid. Although the existing various NILM algorithms have made significant progress in energy consumption feedback, there are still some problems need to further addressed, such as the exponential growth of state space with the increase of the number of multi-state devices, which leads to the dimension disaster; and it is difficult

To address the repair equipment allocation problem for a support-and-repair ship on a deep sea, a hybrid multi-criteria decision making and optimization approach is designed. Evidence reasoning approach is used to aggregate the evaluation information of quantitative criteria (i.e., weight and economics) and qualitative criteria (i.e., repair ability, reliability and convenience). Then, a mathematical

Polymer electrolyte membrane fuel cells (PEMFC) are a promising technology for economic and environmentally friendly energy production. However, they haven’t reached their full potential in the market yet as only few reliable PEMFC systems have successfully passed the prototyping face. A drawback of the current diagnostic tools is that only a select few are of high genericity, reliability and can perform

Image understanding heavily relies on accurate multi-label classification. In recent years, deep learning algorithms have become very successful for such tasks, and various commercial and open-source APIs have been released for public use. However, these APIs are often trained on different datasets, which, besides affecting their performance, might pose a challenge to their performance evaluation.

The volume of published papers provided by the scientific community has increased over the last years in a drastic way. This fact has led to having a considerable growth of the topics covered by different publications. Despite topics under discussion on these publications were usually regarded as cutting edge subjects when released in conferences and journals, the restless evolution of science may

Almost all the complex interactions between humans, animals, biological cells, neurons, or any other objects are now modeled as a graph with the nodes as the objects of interest and interactions as the edges. The identification of the most central or influential node in such a complex network has many practical applications in diverse domains such as viral marketing, infectious disease spreading, rumor

In this work, we solve the problem of finding the best locations to place stations for depositing/collecting shared bicycles. To do this, we model the problem as the p-median problem, that is a major existing localization problem in optimization. The p-median problem seeks to place a set of facilities (bicycle stations) in a way that minimizes the distance between a set of clients (citizens) and their

Machine learning algorithms provide feasibility for crude oil price prediction. In this paper, a novel multi-hybrid predictive neural network model is proposed based on complex deep learning algorithm, which integrates empirical wavelet transform, random inheritance formula error correction algorithm, deep bidirectional LSTM neural network and Elman recurrent neural network with variational learning

Automated object detection systems are a key component of modern surveillance applications. These systems rely on computationally expensive computer vision algorithms that perform object detection on visual data recorded by surveillance cameras. Due to the security and safety implications of these systems, this visual data st be processed accurately and in real-time. However, many of the frames that

The quest for best practices may lead to an increased risk of poor decision-making, especially when aiming to attain best practice levels reveals that efforts are beyond the organization’s present capabilities. This situation is commonly known as the “best practice trap”. Motivated by such observation, the purpose of the present paper is to develop a practical methodology to support better practice

Word embeddings are substantially successful in capturing semantic relations among words. However, these lexical semantics are difficult to be interpreted. Definition modeling provides a more intuitive way to evaluate embeddings by utilizing them to generate natural language definitions of corresponding words. This task is of great significance for practical application and in-depth understanding of

Cardiac arrhythmia disorder is known as one of the most common diseases in the world. Today, this disease is considered as the leading cause of death in industrial and semi-industrial societies. Various tools and methods have been developed to study the detection of heart diseases, based on analyzing the electrocardiogram (ECG) signal. Due to the simplicity and noninvasive nature, ECG signals are vastly

This paper addresses a new multiple automatic guided vehicle dispatching problem (AGVDP) from material handling process in a matrix manufacturing workshop. The problem aims to determine a solution with the objective of minimizing the transportation cost including travel cost, penalty cost for violating time and AGV cost. For this purpose, a mixed integer linear programming model is first formulated

Epileptic seizures are characterised by abnormal neuronal discharge, causing notable disturbances in electrical activities of the human brain. Traditional methods based on manual approaches applied in seizure detection in electroencephalograms (EEG) have drawbacks (e.g., time constraint, lack of effective feature identification relative to disease symptoms and susceptibility to human errors) that can

The telecommunication industry is a saturated market where a proper implementation of a retention campaign is critical to be competitive, since retaining a customer is cheaper than attracting a new one. Hence, it is crucial to detect customer behavioral patterns and define accurate approaches to predict potential churners. Multiple researchers have used binary classification methods to predict churn

The recent outbreak of the respiratory ailment COVID-19 caused by novel coronavirus SARS-Cov2 is a severe and urgent global concern. In the absence of effective treatments, the main containment strategy is to reduce the contagion by the isolation of infected individuals; however, isolation of unaffected individuals is highly undesirable. To help make rapid decisions on treatment and isolation needs

Conceptualization is to obtain the most appropriate concepts for noun terms (entities) under different contexts, which plays an important role in human knowledge understanding. However, in natural language, entities are often ambiguous, which creates difficulties in conceptualization. To accurately conceptualize, we must eliminate the ambiguity of entities. Existing methods mainly rely on similar or

Most of traditional trajectory clustering algorithms often cluster similar trajectories from a temporal or spatial perspective. One weak point is that the semantic relationship between the trajectories is ignored. In some cases, trajectories with spatio-temporal similarities may be semantically related, and the negligence of semantic information may result in unreasonable trajectory clustering results

During the last years, constrained clustering has emerged as an interesting direction in machine learning research. With constrained clustering, the quality of results can be improved by using constraints if a high-quality set of constraints is selected. Querying beneficial constraints is a challenging task because there is no metric for measuring the quality of constraints before clustering. A new

In the manufacturing industry, the preventive maintenance (PM) is a common practice to reduce random machine failures by replacing/repairing the aged machines or parts. The decision on when and where the preventive maintenance needs to be carried out is nontrivial due to the complex and stochastic nature of a serial production line with intermediate buffers. In order to improve the cost efficiency

Many applications of intelligent systems involve understanding a group of contrastively different outcome (e.g., all survivors of a deadly cancer, a top performing team in a large corporation). The intelligent system needs to identify attributes (features) which best describe or explain the group versus its alternatives. In data mining, this problem is studied under the framework of contrast set mining

In order to find efficient solutions to real complex world problems, computer sciences and especially heuristic algorithms are often used. Heuristic algorithms can give optimal solutions for large scale optimization problems in an acceptable period. Social Spider Algorithm (SSA), which is a heuristic algorithm created on spider behaviors are studied. The original study of this algorithm was proposed

This study explores machine learning methods for the detection of unexpected findings in Spanish radiology reports. Regarding radiological reports, unexpected findings are the set of radiological signs identified at a certain imaging modality exam which meet two characteristics: they are not apparently related with the a priori expected results of the radiological exam and involve a clinical emergency

Image classification is an essential component of expert and intelligent systems. The accuracy and efficiency of image classification algorithms significantly affect the performance of related expert systems. Residual network (ResNet) shows strong superiority in image modeling. However, it has also been proved to be low-efficient. In this study, we proposed a novel channel-wise attention mechanism

Among emerging technologies large attention has been devoted to the so called Fourth Industrial Revolution, or Industry 4.0, which is also a case study of a major industrial policy initiative, led by Germany. In the field of methodologies to profile and monitor emerging technologies the role of Wikipedia has been recently explored. In this paper we extend the use of Wikipedia by comparing the German

Feature-based time series representations have attracted substantial attention in a wide range of time series analysis methods. Recently, the use of time series features for forecast model averaging has been an emerging research focus in the forecasting community. Nonetheless, most of the existing approaches depend on the manual choice of an appropriate set of features. Exploiting machine learning

Recommender systems are large-scale machine learning and knowledge discovery tools aimed at providing personalized recommendations to customers based on their preferences and needs. They need to handle large quantities of diverse and very sparse data in a matter of seconds. Matrix factorization techniques have proven to be useful and reliable for implementing recommender systems, while data sparsity

Given the importance that two-stage Data Envelopment Analysis (DEA) models have attained in recent years, this paper presents a systematic review of the literature on the topic focusing on the banking industry. We discuss the two-stage terminology itself, which is not yet not consolidated. We also discuss the current state-of-the-art and present opportunities, as well as challenges, for future studies

Facial expression recognition plays an important role in research on human-computer interaction. The common facial expressions are mixtures of six basic emotions: anger, disgust, fear, happiness, sadness, and surprise. The current study, however, focused on a single basic emotion on the basis of physiological signals. We proposed emotion distribution learning (EDL) based on surface electromyography

Describing texture is a very challenging problem for many image-based expert and intelligent systems (e.g. defective product detection, people re-identification, abnormality investigation in medical imaging and remote sensing applications…) since the process of texture classification relies on the quality of the extracted features. Indeed, detecting and extracting features is a hard and time-consuming

Skin detection is the process of discriminating skin and non-skin regions in a digital image and it is widely used in several applications ranging from hand gesture analysis to track body parts and face detection. Skin detection is a challenging problem which has drawn extensive attention from the research community in the context of expert and intelligent systems, nevertheless a fair comparison among

In this work, we study a closed-loop supply chain with remanufacturing in a competitive market, where the supply chain is a price taker. A Stackelberg game framework is considered, where a manufacturer (leader) has sufficient channel power over a retailer (follower). We develop analytical models to show that a closed-loop supply chain with remanufacturing in a competitive market can achieve the same

Link prediction exercises may prove particularly challenging with noisy and incomplete networks, such as criminal networks. Also, the link prediction effectiveness may vary across different relations within a social group. We address these issues by assessing the performance of different link prediction algorithms on a mafia organization. The analysis relies on an original dataset manually extracted

Recommendation system (RS) is a technology that provides accurate recommendations to users. However, it is not comprehensive to only consider the accuracy of the recommendation because users have different requirements. To improve the comprehensive performance, this paper presents a hybrid recommendation model based on many-objective optimization, which can simultaneously optimize the accuracy, diversity

Conventional textual documents clustering algorithms suffer from several shortcomings, such as the slow convergence of the immense high-dimensional data, the sensitivity to the initial value, and the understandability of the description of the resulted clusters. Although many clustering algorithms have been developed for English and other languages, very few have tackled the problem of clustering the

The influence maximization problem has gained increasing attention in recent years. Previous research focuses on the development of algorithms to analyze static social networks. However, real social networks are not static but they are represented as dynamic networks that evolve across time. Motivated by this drawback, the purpose of this survey is to highlight the characteristics and challenges of

Human behaviour-based particle swarm optimization (HPSO) is an improved particle swarm optimization (PSO) algorithm, which can effectively increase the diversity of population, convergence speed and precision by changing particles’ flight schema. In this paper, the first- and second-order stabilities of HPSO on the assumption that the personal optimum, the global optimum and the global worst particle

Identification systems based on biometric features are becoming increasingly important. One of the most common biometric features is the ear. The accuracy of these systems is heavily dependent on the characteristics extracted from them. In this paper, an appropriate combination of local and global features in the frequency domain is extracted as unique features of the ear region. In the proposed approach

This study presents a new risk assessment model by combining interval type-2 fuzzy best-worst method (IT2F-BWM) and perceptual reasoning for risk evaluation of digital transformation projects. Digital transformations are technology-driven change processes that transform many businesses. Digital transformations are risky and difficult undertakings so that the decision-makers should be supported with

Scientific articles can be annotated with short sentences, called highlights, providing readers with an at-a-glance overview of the main findings. Highlights are usually manually specified by the authors. This paper presents a supervised approach, based on regression techniques, with the twofold aim at automatically extracting highlights of past articles with missing annotations and simplifying the

Creation of Expert and Intelligent Dialogue/Virtual Agent (VA) that can serve complicated and intricate tasks (need) of the user related to multiple domains and its various intents is indeed quite challenging as it necessitates the agent to concurrently handle multiple subtasks in different domains. This paper presents an expert, unified and a generic Deep Reinforcement Learning (DRL) framework that

This work tackles the Traffic Counting Location Problem (TCLP), where we aim finding the best number and location of counting stations to cover a road network in order to obtain its traffic flows. It is important to reduce deployment, maintenance and operation costs of traffic stations. We propose a progressive hybrid algorithm based on exact, heuristic and hybrid approaches embedded on a set covering

Data scarcity represents an important constraint for the training of deep neural networks in medical imaging. Medical image labeling, especially if pixel-level annotations are required, is an expensive task that needs expert intervention and usually results in a reduced number of annotated samples. In contrast, extensive amounts of unlabeled data are produced in the daily clinical practice, including

Social networking sites have a wealth of user-generated unstructured text for fine-grained sentiment analysis regarding the changing dynamics in the marketplace. In aspect-level sentiment analysis, aspect term extraction (ATE) task identifies the targets of user opinions in the sentence. In the last few years, deep learning approaches significantly improved the performance of aspect extraction. However

To model a human perception-based saliency detection algorithm in cluttered and noisy background images is a challenging problem in computer vision. Recently, many saliency detection algorithms have been proposed, which exploit the background information, or boundary priors of an image, to detect the salient object similar to the human attention system. These algorithms may not provide satisfying detection

Ranking multiple-input and multiple-output units is a critical problem that arises in a broad range of disciplines. While various methods have been proposed and applied, their comparative strengths and weaknesses are not well understood. In this paper, we assess and compare two popular methods, data envelopment analysis (DEA) and heuristic rank aggregation approach (i.e., the Borda method), in the

At an operational level, order picking is the main activity in fulfillment centers. Motivated by and through collaboration with a third party logistic company, this study presents a novel hybrid column generation (CG), genetic algorithm (GA), and artificial neural network (ANN) heuristic for minimizing makespan in manual order picking operations. The results of column generation heuristic is compared

Background and objective Heterogeneous complex networks are large graphs consisting of different types of nodes and edges. The knowledge extraction from these networks is complicated. Moreover, the scale of these networks is steadily increasing. Thus, scalable methods are required. Methods In this paper, two distributed label propagation algorithms for heterogeneous networks, namely DHLP-1 and DHLP-2

In many real-world applications classification problems suffer from class-imbalance. The classification methods for imbalanced data with only data processing or algorithm improvement cannot get satisfied classification performance of the minority class. This paper proposes an ensemble classification method based on model dynamic selection driven by data partition hybrid sampling for imbalanced data