Traditional Data Envelopment Analysis (DEA) models usually consider decision-making units (DMU) as black boxes that consume a set of inputs to produce a set of outputs and ignore the units’ internal structure. In literature, two-stage DEA models have been introduced and developed to deal with the inner workings of such DMUs; however, models in the extant literature can only be used to assess technologies

This paper presents the autoencoder-generative adversarial network (AGAN) in the analysis of mammograms. The AGAN architecture is used to augment the data by generating additional representations of the mammogram images, enhancing this way the information of the analyzed problem. The images generated by this this deep network are appended to the original set of mammograms and fed to the input of convolutional

Background: Analysis and classication of extensive medical data (e.g. electroencephalography (EEG) signals) is a signicant challenge to develop effective brain-computer interface (BCI) system. Therefore, it is necessary to build automated classification framework to decode different brain signals. Methods: In the present study, two-step filtering approach is utilize to achieve resilience towards cognitive

Evolutionary clustering (EC) refers to the applications of evolutionary optimization algorithms such as genetic algorithm to data clustering. Although multi-objective evolutionary clustering algorithms were proposed to simultaneously consider different cluster properties such as compactness and separation, these techniques usually suffer from a reasonable initial population and a pre-defined number

With the development of machine learning technologies, predicting stock movements by analyzing news articles has been studied actively. Most of the existing studies utilize only the datasets of target companies, and some studies use datasets of the relevant companies in the Global Industry Classification Standard (GICS) sectors. However, we show that GICS has a limitation in finding relevance regarding

This work proposes a novel portfolio management method using deep reinforcement learning on markets with a dynamic number of assets. This problem is especially important in cryptocurrency markets, which already support the trading of hundreds of assets with new ones being added every month. A novel neural network architecture is proposed, which is trained using deep reinforcement learning. Our architecture

Finding the best classifiers according to different criteria is often performed by a multi-objective machine learning algorithm. This study considers two criteria that are usually treated as the most important when deciding which classifier to apply in practice: comprehensibility and accuracy. A model that offers a broad range of trade-offs between the two criteria is introduced because they conflict;

In this article, an Improved Grey Wolf Optimizer (I-GWO) is proposed for solving global optimization and engineering design problems. This improvement is proposed to alleviate the lack of population diversity, the imbalance between the exploitation and exploration, and premature convergence of the GWO algorithm. The I-GWO algorithm benefits from a new movement strategy named dimension learning-based

Social media has recently become popular due to its vast applications. The common people all over the world uses its diverse channels to express personal views, experiences and opinions regarding diverse topics. Social media has revolutionized the way people interact and communicate with each other and overall, it has changed the methods and approaches in about all the aspects of life such as social

Objectives With the technological advancements in the field of tele-health monitoring, it is now possible to gather huge amount of electro-physiological signals such as the electrocardiogram (ECG). It is therefore necessary to develop models/algorithms that are capable of analysing these massive amount of data in real-time. This paper proposes a deep learning model for real-time segmentation of heartbeats

A point of interest (POI) recommender system (RS) is one of the representative research areas based on the location-based social network (LBSN). Most POI RS studies utilized various implicit information or social information to improve recommendation accuracy. However, majority of these studies overlooked the importance of users’ initial check-in information. Users are affected by their first input

Learning data representations that reflect the customers’ creditworthiness can improve marketing campaigns, customer relationship management, data and process management or the credit risk assessment in retail banks. In this research, we show that it is possible to steer data representations in the latent space of the Variational Autoencoder (VAE) using a semi-supervised learning framework and a specific

Feature selection is crucial to develop a brain computer interface (BCI) system which has high classification accuracy and less computational complexity in especially a large feature space. Feature selection (FS) problem has been solved by many various methods. Among these methods, especially evolutionary computation (EC) techniques have gained a lot of attention in recent years. However, there are

Brain magnetic resonance images (MRI) suffer from many artifacts such as noise and intensity inhomogeneity. Moreover, they contain an abundant amount of fine image structures, edges, and corners in various areas of the image. These anomalies and structural complexities affect the segmentation process of the brain MRI which is required by physicians for the diagnosis purpose. Recently, we have proposed

A high proportion of older adults with type 2 diabetes (T2D) often develop cardiovascular diseases (CVD). Diagnosis and regular monitoring of their multimorbidity is clinically and economically resource intensive. The interconnectedness of their health data and disease progression pathways can potentially reveal the multimorbidity risk if carefully analysed by data mining and network analysis techniques

Design failures often incur substantial cost overruns in the shipbuilding industry. Precautions against possible design failures facilitate on-time delivery and improved productivity. However, few studies have investigated the use of accumulated knowledge to prevent ship design failure. In addition, existing associative classification (AC) methods pay little attention to the rule consolidation process

Twitter is the third most popular worldwide Online Social Network (OSN) after Facebook and Instagram. Compared to other OSNs, it has a simple data model and a straightforward data access API. This makes it ideal for social network studies attempting to analyze the patterns of online behaviour, the structure of the social graph, the sentiment towards various entities and the nature of malicious attacks

Recognition of cursive handwritten images has advanced well with recent recurrent architectures and attention mechanism. Most of the works focus on improving transcription performance in terms of Character Error Rate (CER) and Word Error Rate (WER). Existing models are too slow to train and test networks. Furthermore, recent studies have recommended models be not only efficient in terms of task performance

With the development of information and telecommunication technology and the wide adoption of smartphones, consumers gradually change their purchase pattern toward online shopping. They can order products from their smartphones at any moment from any place, and the volume and variety of products delivered to consumers are increasing explosively. Companies in this industry need to set up the operational

Environmental protection and intelligence have become the inevitable development trend of future transportation. Connected and automated vehicles (CAVs) are expected to be applied in the near future. In this context, how to schedule CAVs to meet customer demands with carbon emissions minimization has become a new green vehicle routing problem (VRP). Due to the fact that carbon emissions are tremendously

Detection of power lines in aerial images is an important problem to prevent accidents of unmanned aerial vehicles operating at low altitudes in the electrical industry. Recently, pixel-level power line detection using deep learning has been studied but production of the pixel-level annotations for massive dataset is difficult. In this study, we propose a power line detection algorithm using weakly

A major part of the balance sheets of the largest U.S. banks consists of credit card portfolios. Hence, managing the charge-off rates is a vital task for the profitability of the credit card industry. Different macroeconomic conditions affect individuals’ behavior in paying down their debts. In this paper, we propose an expert system for loss forecasting in the credit card industry using macroeconomic

Credit risk assessment plays a key role for correctly supporting financial institutes in defining their bank policies and commercial strategies. Over the last decade, the emerging of social lending platforms has disrupted traditional services for credit risk assessment. Through these platforms, lenders and borrowers can easily interact among them without any involvement of financial institutes. In

Corporate credit rating assessment is one of the crucial problems of credit risk management; it will help the financial institutions and government decide whether to issue debts. Recent studies focusing on the prediction of credit rating by using artificial intelligence (AI) techniques have shown impressive results compared to traditional statistical methods. Although the AI techniques can be used

Optimization problems are defined as the functions whereby the target is to find the optimum state depending on the parameters that have certain limitations. In the field of optimization, the aim is to find from among multiple alternative solutions the optimal solution or approximate solution that provides all the restrictions. Metaheuristic is an extremely effective method to find approximate solutions

Existing neural collaborative filtering (NCF) recommendation methods suffer from severe sparsity problem. Knowledge Graph (KG), which commonly consists of fruitful connected facts about items, presents an unprecedented opportunity to alleviate the sparsity problem. However, pure NCF models can hardly model the high-order connectivity in KG, and ignores complex pairwise correlations between user/item

Multi-view subspace clustering has made remarkable achievements in the field of multi-view learning for high-dimensional data. However, many existing multi-view subspace clustering methods still have two disadvantages. First, most of them only recover the subspace structure from either consistent or specific perspective. Second, they often fail to take advantage of the high-order information among

This paper presents a novel approach for performance appraisal and ranking of decision-making units (DMUs) with two-stage network structure in the presence of imprecise and vague data. In order to achieve this goal, two-stage data envelopment analysis (DEA) model, adjustable possibilistic programming (APP), and chance-constrained programming (CCP) are applied to propose the new fuzzy network data envelopment

This paper studies the effectiveness of estimating credit rating transition matrices using sequence-based clustering on historical credit rating sequences. The data set used in this study consisted of monthly credit rating sequences from Korean companies from 1986 to 2018. The credit rating sequences were converted to sequence matrices and was clustered using PCA-guided K-means. Representative transition

Searching for new biometric traits is currently a necessity because traditional ones such as fingerprint, voice, or face are highly prone to forgery. For this reason, the study of bioelectric signals has the potential to develop new biometric systems. A motivation for using electroencephalogram signals is that they are unique to each person and are much more difficult to replicate than conventional

Lung cancer is one of the most common diseases for human beings everywhere throughout the world. Early identification of this disease is the main conceivable approach to enhance the possibility of patients’ survival. In this paper, a k-Nearest-Neighbors technique, for which a genetic algorithm is applied for the efficient feature selection to reduce the dataset dimensions and enhance the classifier

Link prediction is an important issue in complex network analysis and mining. Given the structure of a network, a link prediction algorithm obtains the probability that a link is established between two non-adjacent nodes in the future snapshots of the network. Many of the available link prediction methods are based on common neighborhood. A problem with these methods is that if two nodes do not have

This paper aims to develop a scenario-based optimization framework to deals with several issues related to redesign sustainable reverse supply chain; focused particularly in the epistemic uncertainty of supply and demand input parameters and their relationship with supply chain performance, both important for the redesign problem. The two-step optimization framework starts with a Fuzzy Inference System

Pythagorean fuzzy (PF) sets involving Pythagorean membership grades can befittingly manipulate inexact and equivocal information in real-life problems involving multiple criteria decision analysis (MCDA). The linear programming technique for multidimensional analysis of preference (LINMAP) is a prototypical compromising model, and it is widely used to carry on decision-making problems in many down-to-earth

While recent years have witnessed the remarkable success of deep learning methods in automated skin lesion detection systems, there still exists a gap between manual assessment of experts and automated evaluation of computers. The reason behind such a gap is the deep learning models demand considerable amounts of data, while the availability of annotated images is often limited. Data Augmentation (DA)

Black-box Artificial Intelligence (AI) methods, e.g. deep neural networks, have been widely utilized to build predictive models that can extract complex relationships in a dataset and make predictions for new unseen data records. However, it is difficult to trust decisions made by such methods since their inner working and decision logic is hidden from the user. Explainable Artificial Intelligence

Multimodal data fusion (MMDF) is the process of combining disparate data streams (of different dimensionality, resolution, type, etc.) to generate information in a form that is more understandable or usable. Despite the explosion of data availability in recent decades, as yet there is no well-developed theoretical basis for multimodal data fusion, i.e., no way to determine a priori which approach is

Educational timetabling is an ongoing challenging administrative task that is required in most academic institutions. This is mainly due to a large number of constraints and requirements that have to be satisfied. Educational timetabling problems have been classified as NP-hard problems and can be divided into three types: exam timetabling, course timetabling and high school timetabling. The domain

An innovative neurodynamical model of epidemics in social networks – the Neuro-SIR – is introduced. Susceptible–Infected–Removed (SIR) epidemic processes are mechanistically modeled as analogous to the activity propagation in neuronal populations. The workings of infection transmission from individual to individual through a network of social contacts, is driven by the dynamics of the threshold mechanism

Network alignment, the problem of identifying similar nodes across networks, is an emerging research topic due to its ubiquitous applications in many data domains such as social-network reconciliation and protein-network analysis. While traditional alignment methods struggle to scale to large graphs, the state-of-the-art representation-based methods often rely on pre-defined anchor links, which are

Artificial Immune Networks (AIN) is a population-based evolutionary algorithm that is inspired by theoretical immunology. It applies ideas and metaphors from the biological immune system to solve multi-disciplinary problems. This paper presents a novel application of the AIN for optimizing shallow machine learning (ML) classification algorithms. AIN accomplishes this task by searching the best hyper-parameter

We survey research on self-driving cars published in the literature focusing on autonomous cars developed since the DARPA challenges, which are equipped with an autonomy system that can be categorized as SAE level 3 or higher. The architecture of the autonomy system of self-driving cars is typically organized into the perception system and the decision-making system. The perception system is generally

This research proposes a reinforced salp swarm algorithm (SSA) variant with an ensemble mutation strategy and a restart mechanism, which is named CMSRSSSA for short, to enhance exploration and exploitation capacity of SSA and conquer the restriction of a single search mechanism of the SSA in tackling continuous optimization problems. In this variant, an ensemble/composite mutation strategy (CMS) can

In artificial intelligence, an expert/intelligent systems can emulate the decision-making ability of human experts. A good classification algorithm can provide significant assistance to expert/intelligent systems in solving a variety of practical problems. In classification, the “hard” instances may be outliers or noisy instances that are difficult to learn, which may confuse the classifier and induce

Magnetic Resonance Imaging MRI is a relevant tool for breast cancer screening. Moreover, an accurate 3D segmentation of breast tumors from MRI scans plays a key role in the analysis of the disease. In this manuscript, we propose a novel 3D automatic method for segmenting MRI breast tumors, called 3D Automatic Levels Propagation Approach (3D-ALPA). The proposed method performs the segmentation automatically

Integrating return prediction of traditional time series models in portfolio formation can improve the performance of original portfolio optimization model. Since machine learning and deep learning models have shown overwhelming superiority than time series models, this paper combines return prediction in portfolio formation with two machine learning models, i.e., random forest (RF) and support vector

Graph convolutional networks (GCNs) and network embedding are the two main categories of popular methods for Semi-Supervised Node Classification (SSNC) in social network. However, the former is commonly oriented to attributed networks with efficient auxiliary information in nodes. The latter is usually not geared towards specific graph mining tasks. Therefore, these methods often perform poorly for

Functional clustering is based on functional similarity measures that are adapted to functional data. However, the existing functional similarity measures account either for the position (value) or temporal deviation (bias) of extreme points of the functional curves. This may lead to erroneous conclusions on the similarities of the curves. In this case, most functional clustering measures underperform

In order to improve the classification accuracy, a preprocessing step is used to pre-filter some redundant or irrelevant features before decision tree construction. And a new feature selection algorithm FWDT is proposed based on this. Experimental results show that FWDT our proposed method performs better for the measures of accuracy, recall and F1-score. Furthermore, it can reduce the required time

Finding feasible solutions to real-world problems is a crucial task. Metaheuristic algorithms are widely used in many fields due to the variety of solutions they can produce. The grey wolf optimizer (GWO) is a relatively novel population-based metaheuristic algorithm that has been shown to have good optimization performance. However, due to the insufficient diversity of wolves in some cases, this approach

With the advent of the era of big data, the recommendation system has become an effective solution to the problem of information overload. This paper takes the literature data related to the recommendation system theme from 2009 to 2018 and included in the core collection of Web of Science database as the research object, and utilizes bibliometric methods to analyze the theme of recommendation system

Novel coronavirus disease (nCOVID-19) is the most challenging problem for the world. The disease is caused by severe acute respiratory syndrome coronavirus-2 (SARS-COV-2), leading to high morbidity and mortality worldwide. The study reveals that infected patients exhibit distinct radiographic visual characteristics along with fever, dry cough, fatigue, dyspnea, etc. Chest X-Ray (CXR) is one of the

Text classification, using deep learning techniques, has become a research challenge in natural language processing. Most of the existing deep learning models for text classification face difficulties when the length of the input text increases. Most models work well on shorter text inputs, however, their performance degrades with the increase in the input length. In this work, we introduce a model

Analyzing and learning from high dimensional data have always been challenging in machine learning tasks, causing serious computational complexities and poor learning performances. Supervised dimensionality reduction is a popular technique to address such challenges in supervised learning tasks, where data are accompanied with labels. Traditionally, such techniques mostly learn one single transformation

Many intelligent systems in business, government or academy process natural language as an input for their inference or they might even communicate with users in natural language. The natural language processing within them is currently often done utilizing machine learning models. However, machine learning needs training data and such data are often absent for low-resource languages. The lack of data

Belief Rule-Based (BRB) model has been widely used for complex system classification and prediction. However, excessive antecedent attributes will cause the combinatorial explosion problem, which restricts the applicability of the BRB model to high-dimensional problems. In this paper, we propose an Ensemble-BRB model with the use of the bagging framework to downsize the belief rule base and avoid the

In this paper, a novel approach based on non-inverse matrix online sequence extreme learning machine (NIOSELM) for phishing detection is presented, which takes into account three types of features to comprehensively characterize a website. For the NIOSELM algorithm, we use Sherman Morriso Woodbury equation to avoid the matrix inversion operation, and introduce the idea of online sequence extreme learning

The optimization problems are divided into a single objective and multi-objective. Single objective optimization has only one objective function; whereas, multi-objective optimization has multiple objective functions that generate the Pareto set; therefore, solving a multi-objective problem is a challenging problem. This paper presents a new multi-objective optimization method (called MWDEO) based

This paper presents an effective Progressive Hedging algorithm for vehicle routing problem with two-layers time window assignment and stochastic service times (2L-TWAVRPSST). Based on a predefined exogenous time window determined by the customers, an endogenous time window is assigned to each customer. Endogenous time windows have flexible width and composed of two-layers. The outer layer is wider