Three Dimensional Field Programmable Gate Arrays (3D FPGAs) recently are presented as the next generation of the FPGA family to continue the integration of more transistors on a single chip seamlessly. The 3D FPGA are fabricated by stacking several layers of semiconductor substrates and the interconnection among layers are realized using Through Silicon Vias (TSVs). Despite their benefits regarding

Aspect Terms Extraction (ATE) and Aspect Categories Detection (ACD) are two fundamental sub-tasks for aspect-based sentiment analysis. Most of the existing works mainly focus on the ATE task or the co-extraction of aspect terms and opinion words, while few attention are paid to the ACD task. In this work, we propose a joint model to seamlessly integrate the ATE and ACD tasks into a multi-task learning

The credit scoring system has been revolutionized with the development of the financial system and has received increasing attention from the academia and industry. Artificial intelligence technology has reshaped credit scoring through predictive classification. In this study, a new hybrid ensemble model with voting-based outlier detection and balanced sampling is proposed to achieve superior predictive

Graph convolutional neural networks (GCNNs) have received much attention recently, owing to their capability in handling graph-structured data. Among the existing GCNNs, many methods can be viewed as instances of a neural message passing motif; features of nodes are passed around their neighbors, aggregated and transformed to produce better nodes’ representations. Nevertheless, these methods seldom

This paper presents an alternative global optimization meta-heuristics (MHs) approach, inspired by the natural selection theory. The proposed approach depends on the competition among six MHs that allows generating an offspring, which can breed the high characteristics of parents since they are unique and competitive. Therefore, this leads to improve the convergence of the solutions towards an optimal

Task scheduling is an effective approach to increase the value of a satellite mission, which leads to improved resource management and quality of service. This work improves the energy prediction model and a task scheduling formulation, expressed in integer programming to maximize the number of tasks performed in nanosatellite missions. A realistic battery model is introduced in the formulation to

Instance-based learning has been proved to be effective for credit assessment in Peer-to-peer(P2P) lending. A key challenge of this application is how to measure the similarity of loans, which have usually multiple features gained from different data sources and models. In this paper, a new similarity measure is proposed to effectively integrate the information from different sources and models for

Machine Learning (ML) is a branch of artificial intelligence that studies algorithms able to learn autonomously, directly from the input data. Over the last decade, ML techniques have made a huge leap forward, as demonstrated by Deep Learning (DL) algorithms implemented by autonomous driving cars, or by electronic strategy games. Hence, researchers have started to consider ML also for applications

Hand Gesture Recognition (HGR) serves as a fundamental way of communication and interaction for human being. While HGR can be applied in Human Computer Interaction (HCI) to facilitate user interaction, it can also be utilized for bridging the language barrier. For instance, HGR can be utilized to recognize sign language, which is a visual language represented by hand gestures and used by the deaf and

The ability to quickly and accurately triage a person’s medical condition in an emergency situation or other critical scenarios could mean the difference between life and death. Endowing a robotic system with vision and tactile capabilities, similar to those of medical professionals, and thus enabling robots to assess a patient’s status in an emergency is a highly sought after characteristic in healthcare

In order to deploy deep Convolutional Neural Networks (CNNs) on the mobile devices, many mobile CNNs are introduced. Currently, some online applications are usually re-trained because of the constantly-increasing data. However, compared with the regular models, it is not very efficient to train the present mobile models. Therefore, the purpose of this paper is to propose efficient mobile models both

Feature selection, an optimization problem, becomes an important pre-process tool in data mining, which simultaneously aims at minimizing feature-size and maximizing model generalization. Because of large search space, conventional optimization methods often fail to generate global optimum solution. A variety of hybrid techniques merging different search strategies have been proposed in feature selection

Age variation is a major problem in the area of face recognition under uncontrolled environments such as pose variation, lighting effects, expression etc. Most of the works of this area have used discriminative feature descriptors. These discriminative feature descriptors are based on their fixed encoding which considers pixels of different radial widths for feature extraction and ignores some radii

Document clustering in text mining is a problem that is heavily researched upon. It is observed that individual approaches based on statistical features and semantic features have been extensively used to solve this problem. However, techniques combining the advantages of both types of features have not been frequently researched upon. Specifically, when the growth in the size of textual data is immense

K-Means algorithm is probably the most famous and popular clustering algorithm in the world. K-Means algorithm has the advantages of simple structure, easy implementation, high efficiency, fast convergence speed, and good results. It has been widely used in many applications, and many extensions of K-Means have been proposed. Basically, most K-Means variants deal with static data. Recently, the dynamic

In deep learning models, the inputs to the network are processed using activation functions to generate the output corresponding to these inputs. Deep learning models are of particular importance in analyzing big data with numerous parameters and forecasting and are useful for image processing, natural language processing, object recognition, and financial forecasting. Sigmoid and tangent activation

Electroluminescence (EL) imaging has become the standard test procedure for defect detection throughout the production, installation and operation stages of solar modules. Using this test, defects such as micro cracks, broken cells, and finger interruptions on photovoltaic modules could be easily detected and potential power loss issues could be effectively addressed. Although EL test is a very powerful

Electric vehicles are environmental transportation modes that are widely applied in green logistics systems. To guarantee the energy efficiency, the impacts of customer service modes and recharging strategies need to be integrated into the optimization of electric logistics resource. This paper proposes an electric vehicle routing problem with mixed backhauls, time windows, and recharging strategies

Research of airlines shows that seat inventory control and therefore, revenue management is based not on a systematic analysis but more on human judgement. Machine learning models have been developed and applied to support decisions for ticket pricing dynamically. However, conventional models and approaches yield low statistical evaluation scores. In this study, the features used in other studies were

A generative adversarial network (GAN) based fully automatic electrocardiogram (ECG) arrhythmia classification system with high performance is presented in this paper. The generator (G) in our GAN is designed to generate various coupling matrix inputs conditioned on different arrhythmia classes for data augmentation. Our designed discriminator (D) is trained on both real and generated ECG coupling

As a meta-heuristic algorithm, bat algorithm (BA) is based on the characteristics of bat-based echolocation and has been widely used in various aspects of optimization problems since it appeared. However, the original BA still has many shortcomings, such as insufficient local search ability, lack of diversity and poor performance on high-dimensional optimization problems. To overcome these weaknesses

Instagram is a free photo-sharing platform where each user has a profile and can upload photos for followers to view, like, and comment. Abusive comments on images can be humiliating and harmful to those who share photos. Developing a comment filter in languages other than English is difficult and time-consuming. This paper proposes a dataset called Abusive Turkish Comments (ATC) to detect abusive

Detecting a counterfeit coin using 2D image processing is nearly impossible in some cases, especially when the coin is damaged, corroded or worn out. Edge detection is one of the most widely used techniques to extract features from 2D images. However, in 2D images, the height information is missing, losing the hidden characteristics. In this paper, we propose a 3D approach to detect and analyze the

Manufacturing industries frequently encounter production scheduling problems containing device dynamic reconfiguration processes (DRP). DRP refers to dynamic device adjustments (such as replacement of tools), leading to changes in the devices’ actual processing time. It has a severe impact on the production schedule. Nevertheless, there is scarcely research upon hybrid flow shop scheduling problem

Catastrophic events create uncertain environments in which it becomes very diffcult to locate affected people and provide aids. People turn to Twitter during disasters for requesting help and/or providing relief to others than their friends and family. A huge number of posts issued online for seeking help could not properly be detected and remained concealed because tweets are noisy and stinky. Existing

Sensor-node localization is among the greatest concerns in the field of wireless sensor networks. Range-based localization techniques generally outperform range-free techniques, particularly in terms of their accuracy. Range-based localization techniques depend on a popular distance estimation method, which requires conversion from a received signal strength indicator to distances. In a case where

Traditional data envelopment analysis (DEA) models mainly set the furthest targets as the frontier projection for inefficient decision-making units (DMUs). To achieve the efficient status with less effort, the closest target models are introduced which projected the least input/output improvement for inefficient DMUs. However, these works typically consider each DMU as a “black box” in the closest

Stress has been identified as one of major contributing factors in car crashes due to its negative impact on driving performance. It is in urgent need that the stress levels of drivers can be detected in real time with high accuracy so that intervening or navigating measures can be taken in time to mitigate the situation. Existing driver stress detection models mainly rely on traditional machine learning

Social and medico-social centers are the main structures in France where different categories of vulnerable populations are hosted. In addition to the daily care, these centers have to ensure the implementation of the personalized project for each resident in response to his/her needs in comply with national legal provisions. This work deals with the main issue of elaborating feasible and thoughtful

Multi-view clustering has attracted increasing attention in recent years since many real data sets are usually gathered from different sources or described by different feature types. Amongst various existing multi-view clustering algorithms, those that are based on non-negative matrix factorization (NMF) have exhibited superior performance. However, NMF decomposing original data directly fails to

As a result of the widespread use of power electronic equipment and the increase in consumption, the importance of effective energy policies and the smart grid begins to increase. Nonlinear loads and other loads in electric power systems are considered as the main reason for power quality disturbance. Distortions in signal quality and shape due to power quality disturbance cause a decrease in total

With the significant growth of multiprocessor systems (MPS) to deal with complex tasks and speed up their execution, the energy generated as a result of this growth becomes one of the significant limits to that growth. Although several traditional techniques are available to deal with this challenge, they don’t deal with this problem as multi-objective to optimize both energy and makespan metrics at

The purpose of this paper is to develop a framework to identify, analyze, and to assess supply chain disruption factors and drivers. Based on an empirical analysis, four disruption factor categories including natural, human-made, system accidents, and financials with a total of sixteen disruption drivers are identified and examined in a real-world industrial setting. This research utilizes an integrated

Machine learning is integrated nowadays in many data-driven applications that attempt to model the behavior of a system. Thus, the implementation of machine-learning algorithms for medical applications is growing, enabling doctors to make decisions based on the output of the model of the system’s behavior. The upper airway is involved in a variety of disorders that lead to non-specific symptoms; thus

The number of spectral bands obtained by hyperspectral sensors improves the ability to distinguish physical objects and materials. But it also brings new challenges to image classification and analysis. In this study, a novel deep learning-based hybrid model called CNN-CVWNN is presented for the hyperspectral images classification (HSIs). The model uses a convolutional neural network (CNN) to extract

P2P Lending recommender systems are embracing portraying schemes to obtain profiles of both loan and lender, and thus to overcome inherent limitations of general recommendation models. A successful recommendation method requires proper handling the interactions between loans and lenders. We argue that three fundamental problems need to be addressed: 1) how to fully utilize different properties of loan

Dimensionality Reduction (DR) is useful to understand high-dimensional data. It attracts wide attention from industry and academia and is employed in areas such as machine learning, data mining and pattern recognition. This work presents a geometric approach to DR termed Polygonal Coordinate System (PCS), capable of representing multidimensional data in two or three dimensions while preserving their

This paper presents a modification to be added to multiclass classifiers, that improves their performance when classifying, in this case, defects appearing in polyethylene films. It aims to classify a new defect by confronting every defect type against each of the other types. In a simplified way, the type that results winner in more matches is the type that the defect belongs to. Different ways of

Feature Selection and classification have previously been widely applied in various fields like business, medical and media fields. High dimensionality in datasets is one of the main challenges that has been experienced in classifying data, data mining and sentiment analysis. Irrelevant and redundant attributes have also had a negative impact on complexity and operation of algorithms for classifying

Most of the extant studies in social recommender system are based on explicit social relationships, while the potential of implicit relationships in the heterogeneous social networks remains largely unexplored. This study proposes a new approach to designing a recommender system by employing grey relational analysis on the heterogeneous social networks. It starts with the establishment of heterogeneous

Current software systems are inherently complex and this fact strongly complicates, and makes more expensive, to validate them. Therefore, it is a must to provide methodologies, supported by tools, that can direct validation activities so that they focus on specific aspects of the system (e.g. its critical parts, common errors produced by developers, components that are expensive to fix after deployment

To keep up with the growing interest of using Web shops for product comparison, we have developed a method that targets the problem of product duplicate detection. If duplicates can be discovered correctly and quickly, customers can compare products in an efficient manner. We build upon the state-of-the-art Multi-component Similarity Method (MSM) for product duplicate detection by developing an automated

Nowadays, recommender systems play an important role in several Location-Based Social Networks (LBSNs). The current advances have considered the trade-off between accuracy and diversity to help users to discover and explore new points-of-interest (POI). However, differently from traditional recommendation scenarios, other equally relevant dimensions (e.g., social and geographical user information)

When a disaster strikes, it is important to allocate limited disaster relief resources to those in need. This paper considers the allocation of resources in humanitarian logistics using three critical performance indicators: efficiency, effectiveness and equity. Three separate costs are considered to represent these metrics, namely, the accessibility-based delivery cost, the starting state-based deprivation

Exploring user interest behind massive user behaviors is essential for online recommendations. Although recommendation models have been proposed recently with great success, existing studies ignore not only the timeliness of online users’ behaviors in terms of their interest, but also the sequential characteristics of their behaviors. To overcome this limitation, we construct a User Movie Interest

In the field of detection and prediction of company defaults and bankruptcy, significant effort has been devoted to evaluating financial ratios as predictors using statistical models and machine learning techniques. This problem becomes crucially important when financial decision-makers are provided with predictions on which to act, based on the output of prediction models. However, research has shown

Connecting candidates and jobs to promote real placement opportunities is one of the most impacting and challenging scenarios for Recommender Systems (RSs). A major concern when building RSs for recruitment services is ensuring placement opportunities for all candidates and jobs as soon as possible, avoiding financial losses for both sides. We refer to these scenarios where candidates or jobs suffer

This study proposes a new method of incorporating emotions from newspaper articles into macroeconomic forecasts, attempting to forecast industrial production and consumer prices leveraging narrative and sentiment from global newspapers. For the most part, existing research includes positive and negative tone only to improve macroeconomic forecasts, focusing predominantly on large economies such as

Recommendation models are vital to the success of recommender systems. The latent factor model is one of the outstanding collaborative recommendation models. It faces many difficulties due to the lack of quality and quantity of preferences observed from users. An effective approach is to use both types of user preferences, which are implicit and explicit, in the latent factor models. In this paper

Construction projects are often deemed as complex and high-risk endeavours, mostly because of their vulnerability to external conditions as well as project-related uncertainties. Risk management (RM) is a critical success factor for companies operating in the construction industry. RM is a knowledge-intensive process that requires effective management of risk-related knowledge. Although some research

Learning style plays a vital role in helping students retain learned concepts for a longer time and also improves the understanding of the concepts. Learning styles in offline and online scenarios are recognized using questionnaires. The recent trend is to identify and use attributes to detect the learning style of the learner automatically without disturbing the learner. The paper is an extension

Class imbalance with overlap is a very challenging problem in electronic fraud transaction detection. Fraudsters have racked their brains to make a fraud transaction as similar as a genuine one in order to avoid being found. Therefore, lots of data of fraud transactions overlap with genuine transactions so that it is hard to distinguish them. However, most attention has been focused on class imbalance

The efficacy and simplicity of using only function evaluations in zeroth-order stochastic optimization (ZOSO) makes it achieve great attention in solving large scale learning tasks. However, the question of how to choose an appropriate step size sequence timely for ZOSO has been less researched. To fill this defect, this paper provides a fast automatic step size selection approach by using an improved

We propose a new methodology for trading financial instruments based on deterministic sign patterns. These patterns are obtained from the m-dimensional elementary sample space consisting of -1,1m, the two possible signs for trading and with m varying. The collection of all possible sign combinations coming from this sample space creates a zero-cost trading strategy and we consider strategies that are

Online harassment is a major threat to users of social media platforms, especially young adults and women. It can cause mental illnesses and impacts deeply and negatively economic institutions experiencing cyberbully attacks by losing their credibility and business. This makes automatic detection of online harassment extremely important. Most of current studies within this context apply machine-learning

We propose a model to solve a project scheduling problem where resource assignments and activity schedules need to be determined to achieve a set of due-date requirements as well as possible. The concept of activity duration tolerance levels is introduced to describe the longest activity durations over which the due-dates are guaranteed to be achieved. Based on this, we propose a due-date achievement

One of the main challenges in times of sanitary emergency is to quickly develop computer aided diagnosis systems with a limited number of available samples due to the novelty, complexity of the case and the urgency of its implementation. This is the case during the current pandemic of COVID-19. This pathogen primarily infects the respiratory system of the afflicted, resulting in pneumonia and in a

Lag-selection is a high dimensional hyper-parameter in the fuzzy time series (FTS) which requires complex optimization process and computational capacity particularly in high frequency dataset (e.g. daily, hourly). Multivariate high order FTS suffers from establishing long logical relationships, and the difficulty of rule matching is proportional to the time lags and number of variables. In the vast

In real markets, generating a smooth implied volatility surface requires an interpolation of the calibrated parameters by using smooth parametric functions. For this interpolation, practitioners do not use all the discrete parameter points but manually select candidate parameter points through time-consuming adjustments (e.g., removing outliers, comparing with the surface from the previous day, and