
显示样式: 排序: IF: - GO 导出
-
A Position Weighted Information Based Word Embedding Model for Machine Translation Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-11-30 Zhen Li; Dan Qu; Yanxia Li; Chaojie Xie; Qi Chen
Deep learning technology promotes the development of neural network machine translation (NMT). End-to-End (E2E) has become the mainstream in NMT. It uses word vectors as the initial value of the input layer. The effect of word vector model directly affects the accuracy of E2E-NMT. Researchers have proposed many approaches to learn word representations and have achieved significant results. However
-
An Initialization-free Distributed Algorithm for Power Dispatch Problem with Multiple Resources of Future Distribution Network Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-11-30 Zixia Sang; Jiaqi Huang; Dongjun Yang; Jiong Yan; Zhi Du; Rengcun Fang
With the gradual development of bidirectional interacted future distribution network, it is necessary to enter distributed clean power sources with various characteristics, including wind turbine and solar panel. Traditional centralized control has difficulties to fulfill the demands of future distribution networks for safe, stable, and efficient operation. Aiming at the constrained power allocation
-
A Hybrid Parameter Estimation for Multi-asset Modeling and Dynamic Allocation Based on Financial Market Microstructure Model Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-11-30 Yemei Qin; Yangyu Zhong; Zhen Lei; Hui Peng; Feng Zhou; Ping Tan
In the previous works, a discrete-time microstructure (DTMS) model for financial market was constructed by using identification technology and was successfully applied to dynamic asset allocation based on the identified excess demand. However, the initial value setting of the parameters has a great influence on the estimated results of the DTMS model, which may make the estimated model to describe
-
Network Traffic Classification Using Deep Learning Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-11-30 Lei Chen; Jian Liu; Ming Xian
The large amount of network traffic generated by Internet applications brings great challenges to Internet security. In order to facilitate network management and realize automatic classification of network traffic, this paper proposes a network traffic classification model NTCNET based on CNNs. Use open data set to do simulation verification experiment, then compare the test results with a variety
-
Automatic RGBD Object Segmentation Based on MSRM Framework Integrating Depth Value Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-11-30 Guoqing Li; Guoping Zhang; Chanchan Qin; Anqin Lu
In this paper, an automatic RGBD object segmentation method is described. The method integrates depth feature with the cues from RGB images and then uses maximal similarity based region merging (MSRM) method to obtain the segmentation results. Firstly, the depth information is fused to the simple linear iterative clustering (SLIC) method so as to produce superpixels whose boundaries are well adhered
-
GRU-corr Neural Network Optimized by Improved PSO Algorithm for Time Series Prediction Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-11-30 Shao-Pei Ji; Yu-Long Meng; Liang Yan; Gui-Shan Dong; Dong Liu
Time series data from real problems have nonlinear, non-smooth, and multi-scale composite characteristics. This paper first proposes a gated recurrent unit-correction (GRU-corr) network model, which adds a correction layer to the GRU neural network. Then, a adaptive staged variation PSO (ASPSO) is proposed. Finally, to overcome the drawbacks of the imprecise selection of the GRU-corr network parameters
-
Absolute Depth Measurement of Objects Based on Monocular Vision Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-11-30 Zhongsheng Wang; Yufeng Lai; Sen Yang; Jiaqiong Gao
With the continuous development of computer vision technology and the continuous upgrading of digital imaging equipment, image depth measurement method is widely used in the fields of intelligent robotics, traffic assistance, three-dimensional modeling and three dimensional video production. The following are the drawbacks of the traditional depth information measurement method: the operation is complex
-
A Seismic Image Denoising Method Based on Kernel-prediction CNN Architecture Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-11-30 Li Lou; Yong Li
To filter noises and preserve the details of seismic images, a denoising method based on kernel prediction convolution neural network (CNN) architecture is proposed. The method consists of two convolution layers and a residual connection, containing a source sensing encoder, a spatial feature extractor and a kernel predictor. The scalar kernel was normalized by the softmax function to obtain the denoised
-
Calibration Algorithm for Error Screening Based on Line Structured Light Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-11-30 Baolong Liu; Ruixia Wu; Yu Liu
The 3D measurement system based on line-structured light uses a camera to capture laser stripes due to changing in the shape of an object, and uses the acquired pixel coordinates for 3D reconstruction. System calibration is an important step in 3D measurement. The current camera calibration algorithm research mainly focuses on improving the algorithm itself, and there is less research on the influence
-
Three Dimensional Measurement System for the Dynamic Deformation of Aero-engine Blade Profile Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-11-30 Yongchao Wei; Chunyan Deng; Xingkun Wu; Liangzhong Ao
In order to solve the technical problem of three-dimensional profile measurement of aero-engine blades with high speed rotation, an optical dynamic measurement system for aero-engine blades was developed. Firstly, the system is calibrated by the algorithm of spatial truncation phase calibration to establish the index between truncation phase and spatial coordinates. During the measurement, the deformation
-
A Redundant Manipulator Joint Torque Estimation Method Based on Disturbance Observer Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-11-30 Xun Liu; Yaqiu Liu; Hanchen Zhao
With the continuous development of the robot industry, both industrial robots and collaborative robots are developing towards light type and intelligence. The core issue is that how to improve the dynamic control performance of robots and reduce costs. The accurate torque feedback control can be achieved by introducing a joint torque sensor. The disadvantages brought by it are higher cost and the limited
-
A Key Node Optimization Scheme for Public Bicycles Based on Wavefront Theory Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-11-30 Yali Peng; Ting Liang; Yuxin Yang; Hong Yin; Ping Li; Jiangang Deng
Considering the functional attributes of public bicycle outlets, users’ travel destinations and travel distances, this paper proposes a key node optimization scheme for urban public bicycle networks based on the combination of key nodes and wavefront theory. First analyze the net wave surface flow during peak hours to determine key nodes, then schedule or add nodes to achieve normal diversion in the
-
Fixed-frequency Current Control Method of Islanding Micro-grid Based on Improved Neural Network Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-11-30 Cuizhe Kuang; Meng Xiao; Zexing Chen; Zehuai Liu; Ziqi Wang; Baoqiang Lv; Guoxin Li
In the islanding micro-grid operation mode, due to the lack of support from the large power grid, the voltage of the bus and each node in the network is completely supported by the cooperation of the micro-grid inverters in the grid. Therefore, the control performance of the micro-grid inverter determines the quality of the power supply voltage. This paper proposes an improved AC and DC islanding micro-grid
-
A BERT Fine-tuning Model for Targeted Sentiment Analysis of Chinese Online Course Reviews Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-11-30 Huibing Zhang; Junchao Dong; Liang Min; Peng Bi
Accurate analysis of targeted sentiment in online course reviews helps in understanding emotional changes of learners and improving the course quality. In this paper, we propose a fine-tuned bidirectional encoder representation from transformers (BERT) model for targeted sentiment analysis of course reviews. Specifically, it consists of two parts: binding corporate rules — conditional random field
-
The Internet of Things Based Fault Tolerant Redundancy for Energy Router in the Interacted and Interconnected Micro Grid Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-11-30 Zixia Sang; Rengcun Fang; He Lei; Jiong Yan; Dongjun Yang; Yicong Wang
The distribution energy router (DER) is the core of the interacted and interconnected micro grid in future distribution network, which fully meets the needs of the ubiquitous power internet of things (IOT) based future distribution network. The reliability of micro grid which applies the DER is highly related to its cascaded full-bridge converters. With the redundant full-bridge converters and IOT
-
Research on Logistics Distribution Route Based on Multi-objective Sorting Genetic Algorithm Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-11-30 Jun Zhao; Hui Xiang; Jinbao Li; Jie Liu; Luyao Guo
With the continuous development of society, the social division of labor is further improved, and social production tends to be highly specialized and industrialized. Moreover, enterprise production is increasingly internationalized, and sales are gradually expanding. Therefore, the multi-objective sequencing in logistics distribution is incorporated into the path optimization of the logistics system
-
Language Model Pre-training Method in Machine Translation Based on Named Entity Recognition Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-11-30 Zhen Li; Dan Qu; Chaojie Xie; Wenlin Zhang; Yanxia Li
Neural Machine Translation (NMT) model has become the mainstream technology in machine translation. The supervised neural machine translation model trains with abundant of sentence-level parallel corpora. But for low-resources language or dialect with no such corpus available, it is difficult to achieve good performance. Researchers began to focus on unsupervised neural machine translation (UNMT) that
-
Improved Multi-domain Convolutional Neural Networks Method for Vehicle Tracking Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-11-30 Jianwen Wang; Aimin Li; Y. Pang
In the field of intelligent transportation, background complexity, lighting changes, occlusion, and scale transformation affect the tracking results of moving vehicles in the video. We propose an improved vehicle object tracking algorithm based on Multi-Domain Convolutional Neural Networks (MDNet), combining the instance segmentation method with the MDNet algorithm, adding two attention mechanisms
-
Modeling Method of Tax Management System Based on Artificial Intelligence Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-11-30 HongBiao Li
The transformation of taxation processes and the optimization and modeling of their management systems are hot topics in many disciplines such as public management and computer science. Therefore, the intelligent tax management is used to implement the tax process. Meanwhile, tax indicators are adopted as independent variables, and the Logistic regression model is applied to check the selected cases
-
Research on Automatic Vulnerability Mining Model Based on Knowledge Graph Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-11-30 Ze Chen; Xiaojun Zuo; Botao Hou; Na Dong; Jie Chang
In the information extraction, information sources can be screened according to the characteristics of the target network at the present stage, and the knowledge graph generated thereby can play a role in assisting the security analysis of the general network or power grid control network, mobile Internet and other special networks. In the method proposed in this paper, knowledge reasoning is mainly
-
A Two Consequent Multi-layers Deep Discriminative Approach for Classifying fMRI Images Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-09-30 Abeer M. Mahmoud; Hanen Karamti; Fadwa Alrowais
Functional Magnetic Resonance Imaging (fMRI), for many decades acts as a potential aiding method for diagnosing medical problems. Several successful machine learning algorithms have been proposed in literature to extract valuable knowledge from fMRI. One of these algorithms is the convolutional neural network (CNN) that competent with high capabilities for learning optimal abstractions of fMRI. This
-
A Holistic Approach for Automatic Deep Understanding and Protection of Technical Documents Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-09-30 Nikolaos Bourbakis; Sukarno Mertoguno
A Technical Document (TD) is mainly composed by a set of modalities appropriately structured and associated. These modalities could be NL-text, block diagrams, formulas, tables, graphics, pictures etc. A deep understanding of a TD will be based on the synergistic understanding and associations of these modalities. This paper offers a novel methodology for the implementation of a holistic approach for
-
A Hybrid Concession Mechanism for Negotiating Software Agents in Competitive Environments Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-09-30 Khalid Mansour
This paper presents a new hybrid concession mechanism for negotiating agents. It considers both the current concession behavior of the proposing agent and the concession offered by its opponent in the last counteroffer to create a new offer. The proposed mechanism is a kind of imitating offer generation tactic. The difference is that it uses the first order difference between the two last counteroffers
-
Optimal Consensus Recovery of Multi-agent System Subjected to Agent Failure Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-09-30 Deep Shekhar Acharya; Sudhansu Kumar Mishra
Multi-Agent Systems are susceptible to external disturbances, sensor failures or collapse of communication channel/media. Such failures disconnect the agent network and thereby hamper the consensus of the system. Quick recovery of consensus is vital to continue the normal operation of an agent-based system. However, only limited works in the past have investigated the problem of recovering the consensus
-
A Novel Online Change Point Detection Using an Approximate Random Blanket and the Line Process Energy Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-09-30 A. Belcaid; M. Douimi
In this paper, we focus on the problem of change point detection in piecewise constant signals. This problem is central to several applications such as human activity analysis, speech or image analysis and anomaly detection in genetics. We present a novel window-sliding algorithm for an online change point detection. The proposed approach considers a local blanket of a global Markov Random Field (MRF)
-
A Persian Medical Question Answering System Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-09-30 Hadi Veisi; Hamed Fakour Shandi
A question answering system is a type of information retrieval that takes a question from a user in natural language as the input and returns the best answer to it as the output. In this paper, a medical question answering system in the Persian language is designed and implemented. During this research, a dataset of diseases and drugs is collected and structured. The proposed system includes three
-
Analytical and Simple Form of Shrinkage Functions for Non-Convex Penalty Functions in Fused Lasso Algorithm Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-09-30 Pichid Kittisuwan
In some circumstances, the performance of machine learning (ML) tasks are based on the quality of signal (data) that is processed in these tasks. Therefore, the pre-processing techniques, such as reconstruction and denoising methods, are important techniques in ML tasks. In reconstructed (estimated) method, the fused lasso algorithm with non-convex penalty function is an efficient method when the signal
-
Estimation of Personalized Heterogeneous Treatment Effects Using Concatenation and Augmentation of Feature Vectors Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-08-19 Lev V. Utkin; Mikhail V. Kots; Viacheslav S. Chukanov; Andrei V. Konstantinov; Anna A. Meldo
A new meta-algorithm for estimating the conditional average treatment effects is pro-posed in the paper. The basic idea behind the algorithm is to consider a new dataset consisting of feature vectors produced by means of concatenation of examples from control and treatment groups, which are close to each other. Outcomes of new data are defined as the difference between outcomes of the corresponding
-
Smart Cities — Detecting Humans in Regions of Disasters: Synergy of Drones, Micro-robots in Underground Tunnels Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-08-19 Nikolaos Bourbakis; Iosif Papadakis Ktistakis; Tarek Seleem
The pieces of information that are being collected from regions of disaster is critical as the rapid deployment of the first responders rely on them. Another critical part of that deployment is the acquisition of different types of information (visual, sounds, and others). Even with that information the rescuing teams still face the difficult task of rescuing humans under debris. Some of the constraints
-
Application of Improved Artificial Intelligence with Runner-Root Meta-Heuristic Algorithm for Dairy Products Industry: A Case Study Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-08-19 Alireza Goli; Ehsan Moeini; Ahmad M. Shafiee; Mohammad Zamani; Elham Touti
As the dairy products have a short consumption period, the accurate prediction of their demand is very important for the dairy industry. Accordingly, this research specifically addresses the prediction of dairy product demand (DPD). The main contribution of this research is to provide an integrated framework based on statistical tests, time-series prediction and artificial intelligence with the runner-root
-
Systematic Construction of Neural Forms for Solving Partial Differential Equations Inside Rectangular Domains, Subject to Initial, Boundary and Interface Conditions Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-08-19 Pola Lydia Lagari; Lefteri H. Tsoukalas; Salar Safarkhani; Isaac E. Lagaris
A systematic approach is developed for constructing proper trial solutions to Partial Differential Equations (PDEs) of up to second order, using neural forms that satisfy prescribed initial, boundary and interface conditions. The spatial domain considered is of the rectangular hyper-box type. On each face either Dirichlet or Neumann conditions may apply. Robin conditions may be accommodated as well
-
Variance Counterbalancing for Stochastic Large-scale Learning Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-08-19 Pola Lydia Lagari; Lefteri H. Tsoukalas; Isaac E. Lagaris
Stochastic Gradient Descent (SGD) is perhaps the most frequently used method for large scale training. A common example is training a neural network over a large data set, which amounts to minimizing the corresponding mean squared error (MSE). Since the convergence of SGD is rather slow, acceleration techniques based on the notion of “Mini-Batches” have been developed. All of them however, mimicking
-
Neural Networks as Classification Mechanisms of Complex Human Activities Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-08-19 Anargyros Angeleas; Nikolaos Bourbakis
Within this paper, we present two neural nets for view-independent complex human activity recognition (HAR) from video frames. For our study here, we reduce the number of frames produced by a video sequence given that we can identify activities from a sparsely sampled sequence of body poses, and, at the same time, we are able to reduce the processing complexity and response while hardly affecting the
-
Traffic Sign Recognition Using a Synthetic Data Training Approach Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-08-19 Oualid Araar; Abdenour Amamra; Asma Abdeldaim; Ivan Vitanov
Traffic Sign Recognition (TSR) is a crucial component in many automotive applications, such as driver assistance, sign maintenance, and vehicle autonomy. In this paper, we present an efficient approach to training a machine learning-based TSR solution. In our choice of recognition method, we have opted for convolutional neural networks, which have demonstrated best-in-class performance in previous
-
Deep Learning Based Sentiment Analysis in a Code-Mixed English-Hindi and English-Bengali Social Media Corpus Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-08-19 Anupam Jamatia; Steve Durairaj Swamy; Björn Gambäck; Amitava Das; Swapan Debbarma
Sentiment analysis is a circumstantial analysis of text, identifying the social sentiment to better understand the source material. The article addresses sentiment analysis of an English-Hindi and English-Bengali code-mixed textual corpus collected from social media. Code-mixing is an amalgamation of multiple languages, which previously mainly was associated with spoken language. However, social media
-
Auxiliary Dictionary of Diversity Learning for Face Recognition with a Single Sample Per Person Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-08-19 Weifa Gan; Huixian Yang; Jinfang Zeng; Fan Chen
Face recognition for a single sample per person is challenging due to the lack of sufficient sample information. However, using generic training set to learn an auxiliary dictionary is an effective way to alleviate this problem. Considering generic training sample of diversity, we proposed an algorithm of auxiliary dictionary of diversity learning (ADDL). We first produced virtual face images by mirror
-
Interval Tests and Contractors Based on Optimality Conditions for Bound-Constrained Global Optimization Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-06-18 Laurent Granvilliers
We study the problem of finding the global optimum of a nonlinear real function over an interval box by means of complete search techniques, namely interval branch-and-bound algorithms. Such an algorithm typically generates a tree of boxes from the initial box by alternating branching steps and contraction steps in order to remove non optimal sub-boxes. In this paper, we introduce a new contraction
-
Sparse Deep Neural Network Optimization for Embedded Intelligence Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-06-18 Jia Bi; Steve R. Gunn
Deep neural networks become more popular as its ability to solve very complex pattern recognition problems. However, deep neural networks often need massive computational and memory resources, which is main reason resulting them to be difficult efficiently and entirely running on embedded platforms. This work addresses this problem by saving the computational and memory requirements of deep neural
-
Logical Encoding of Argumentation Frameworks with Higher-order Attacks and Evidential Supports Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-06-18 Claudette Cayrol; Marie-Christine Lagasquie-Schiex
We propose a logical encoding of argumentation frameworks with higher-order interactions (i.e. attacks/supports whose targets are arguments or other attacks/supports) with an evidential meaning for supports. Our purpose is to separate the logical expression of the meaning of an attack or an evidential support (simple or higher-order) from the logical expression of acceptability semantics. We consider
-
Selecting and Combining Classifiers Based on Centrality Measures Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-06-18 Ronan Assumpção Silva; Alceu S. Britto Jr.; Fabricio Enembreck; Robert Sabourin; Luiz S. Oliveira
Centrality measures have been helping to explain the behavior of objects, given their relation, in a wide variety of problems, since sociology to chemistry. This work considers these measures to assess the importance of every classifier belonging to an ensemble of classifiers, aiming to improve a Multiple Classifier System (MCS). Assessing the classifier’s importance by employing centrality measures
-
Possibilistic Networks: Computational Analysis of MAP and MPE Inference Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-06-18 Amélie Levray; Salem Benferhat; Karim Tabia
Possibilistic graphical models are powerful modeling and reasoning tools for uncertain information based on possibility theory. In this paper, we provide an analysis of computational complexity of MAP and MPE queries for possibilistic networks. MAP queries stand for maximum a posteriori explanation while MPE ones stand for most plausible explanation. We show that the decision problems of answering
-
ITE: A Lightweight Implementation of Stratified Reasoning for Constructive Logical Operators Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-06-18 Arnaud Gotlieb; Dusica Marijan; Helge Spieker
Constraint Programming (CP) is a powerful declarative programming paradigm where inference and search are interleaved to find feasible and optimal solutions to various type of constraint systems. However, handling logical connectors with constructive information in CP is notoriously difficult. This paper presents If Then Else (ITE), a lightweight implementation of stratified constructive reasoning
-
Assigning and Scheduling Service Visits in a Mixed Urban/Rural Setting Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-06-18 Mark Antunes; Vincent Armant; Kenneth N. Brown; Daniel Desmond; Guillaume Escamocher; Anne-Marie George; Diarmuid Grimes; Mike O’Keeffe; Yiqing Lin; Barry O’Sullivan; Cemalettin Ozturk; Luis Quesada; Mohamed Siala; Helmut Simonis; Nic Wilson
This papera describes a maintenance scheduling application, which was developed together with an industrial partner. This is a highly combinatorial decision process, to plan and schedule the work of a group of travelling repair technicians, which perform preventive and corrective maintenance tasks at customer locations. Customers are located both in urban areas, where many customers are in close proximity
-
Evaluating the Effects of Modern Storage Devices on the Efficiency of Parallel Machine Learning Algorithms Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-06-18 Leonidas Akritidis; Athanasios Fevgas; Panagiota Tsompanopoulou; Panayiotis Bozanis
Big Data analytics is presently one of the most emerging areas of research for both organizations and enterprises. The requirement for deployment of efficient machine learning algorithms over huge amounts of data led to the development of parallelization frameworks and of specialized libraries (like Mahout and MLlib) which implement the most important among these algorithms. Moreover, the recent advances
-
Building High Performance Explainable Machine Learning Models for Social Media-based Substance Use Prediction Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-06-18 Tao Ding; Fatema Hasan; Warren K. Bickel; Shimei Pan
Social media contain rich information that can be used to help understand human mind and behavior. Social media data, however, are mostly unstructured (e.g., text and image) and a large number of features may be needed to represent them (e.g., we may need millions of unigrams to represent social media texts). Moreover, accurately assessing human behavior is often difficult (e.g., assessing addiction
-
Heterogeneous Island Models and Their Application to Recommender Systems and Electric Vehicle Charging Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-06-18 Štěpán Balcar; Martin Pilát
In this paper we describe a general framework for parallel optimization based on the island model of evolutionary algorithms. The framework runs a number of optimization methods in parallel with periodic communication. In this way, it essentially creates a parallel ensemble of optimization methods. At the same time, the system contains a planner that decides which of the available optimization methods
-
Acoustic Diversity Classification Using Machine Learning Techniques: Towards Automated Marine Big Data Analysis Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-06-18 Emna Hachicha Belghith; François Rioult; Medjber Bouzidi
During the last years, big data has become the new emerging trend that increasingly attracting the attention of the R&D community in several fields (e.g., image processing, database engineering, data mining, artificial intelligence). Marine data is part of these fields which accommodates this growth, hence the appearance of marine big data paradigm that monitoring advocates the assessment of human
-
Enhanced Unsatisfiable Cores for QBF: Weakening Universal to Existential Quantifiers Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-06-18 Viktor Schuppan
We introduce an enhanced notion of unsatisfiable cores for QBF in prenex CNF that allows to weaken universal quantifiers to existential quantifiers in addition to the traditional removal of clauses. The resulting unsatisfiable cores can be different from those of the traditional notion in terms of syntax, standard semantics, and proof-based semantics. This not only gives rise to explanations of unsatisfiability
-
Sentiment Analysis of Teachers Using Social Information in Educational Platform Environments Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-03-31 Nikolaos Spatiotis; Isidoros Perikos; Iosif Mporas; Michael Paraskevas
Learners’ opinions constitute an important source of information that can be useful to teachers and educational instructors in order to improve learning procedures and training activities. By analyzing learners’ actions and extracting data related to their learning behavior, educators can specify proper learning approaches to stimulate learners’ interest and contribute to constructive monitoring of
-
Anticipointment Detection in Event Tweets Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-03-31 F. Kunneman; M. van Mulken; A. van den Bosch
We developed a system to detect positive expectation, disappointment, and satisfaction in tweets that refer to events automatically discovered in the Twitter stream. The emotional content shared on Twitter when referring to public events can provide insights into the presumed and experienced quality of the event. We expected to find a connection between positive expectation and disappointment, a succession
-
Fuzzy Information Diffusion in Twitter by Considering User’s Influence Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-03-31 Andreas Kanavos; Ioannis E. Livieris
Does a post with specific emotional content that is posted on Twitter by an influential user have the capability to affect and even alter the opinions of those who read it? Accordingly, “influential” users affected by this post can then affect their followers so that eventually a large number of users may change their opinions about the subject the aforementioned post was made on? Social Influence
-
Short Semantic Patterns: A Linguistic Pattern Mining Approach for Content Analysis Applied to Hate Speech Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-03-31 Danielly Sorato; Fábio B. Goularte; Renato Fileto
Microblog posts such as tweets frequently contain users’ opinions and thoughts about events, products, people, institutions, etc. However, the usage of social media to prop-agate hate speech is not an uncommon occurrence. Analyzing hateful speech in social media is essential for understanding, fighting and discouraging such actions. We believe that by extracting fragments of text that are semantically
-
Drug-target Interaction Prediction by Metapath2vec Node Embedding in Heterogeneous Network of Interactions Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-02-28 Mina Samizadeh; Behrouz Minaei-Bidgoli
Drug discovery is a complicated, time-consuming and expensive process. The cost for each new molecular entity (NME) is estimated at $1.8 billion. Furthermore, for a new drug to be FDA approved it often takes nearly a decade and approximately 20 new drugs being approved by the US Food and Drug Administration (FDA) each year. Accurately predicting drug-target interactions (DTIs) by computational methods
-
Optimization of Many Objective Pickup and Delivery Problem with Delay Time of Vehicle Using Memetic Decomposition Based Evolutionary Algorithm Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-02-28 Adeem Ali Anwar; Irfan Younas
The pickup and delivery problem (PDP) is a very common and important problem, which has a large number of real-world applications in logistics and transportation. In PDP, customers send transportation requests to pick up an object from one place and deliver it to another place. This problem is under the focus of researchers since the last two decades with multiple variations. In the literature, different
-
Density-based Approach with Dual Optimization for Tracking Community Structure of Increasing Social Networks Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-02-28 Fariza Bouhatem; Ali Ait El Hadj; Fatiha Souam
The rapid evolution of social networks in recent years has focused the attention of researchers to find adequate solutions for the management of these networks. For this purpose, several efficient algorithms dedicated to the tracking and the rapid detection of the community structure have been proposed. In this paper, we propose a novel density-based approach with dual optimization for tracking community
-
Dynamic Hybrid Graph Matching for Unsupervised Video-based Person Re-identification Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2020-02-28 Xiaoyue Xu; Ying Chen; Qiaoyuan Chen
Taking videos as nodes in a graph, graph matching is an effective technique for unsupervised video-based person re-identification (re-ID). However, most of existing methods are sensitive to noisy training data and mainly only focus on visual content relations between query and gallery videos, which may introduce large amount of false positives. To enhance the robustness to training data and alleviate
-
A Guideline-Based Approach for Assisting with the Reproducibility of Experiments in Recommender Systems Evaluation Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2019-12-02 Nikolaos Polatidis; Elias Pimenidis; Andrew Fish; Stelios Kapetanakis
Recommender systems’ evaluation is usually based on predictive accuracy and information retrieval metrics, with better scores meaning recommendations are of higher quality. However, new algorithms are constantly developed and the comparison of results of algorithms within an evaluation framework is difficult since different settings are used in the design and implementation of experiments. In this
-
Estimating Twitter Influential Users by Using Cluster-Based Fusion Methods Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2019-12-02 Andreas Kanavos; Alexandros Georgiou; Christos Makris
A considerable part of social network analysis literature is dedicated to determining which individuals are to be considered as influential in particular social settings. Concretely, Social Influence can be described as the power or even the ability of a person to yet influence the thoughts as well as the actions of other users. So, User Influence stands as a value that depends on the interest of the
-
Data Stream Classification by Dynamic Incremental Semi-Supervised Fuzzy Clustering Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2019-12-02 Gabriella Casalino; Giovanna Castellano; Corrado Mencar
A data stream classification method called DISSFCM (Dynamic Incremental Semi-Supervised FCM) is presented, which is based on an incremental semi-supervised fuzzy clustering algorithm. The method assumes that partially labeled data belonging to different classes are continuously available during time in form of chunks. Each chunk is processed by semi-supervised fuzzy clustering leading to a cluster-based
-
An Effective Variable Neighborhood Search with Perturbation for Location-Routing Problem Int. J. Artif. Intell. Tools (IF 0.689) Pub Date : 2019-11-15 Hua Jiang; Corinne Lucet; Laure Devendeville; Chu-Min Li
Location-Routing Problem (LRP) is a challenging problem in logistics, which combines two types of decision: facility location and vehicle routing. In this paper, we focus on LRP with multiple capacitated depots and one uncapacitated vehicle per depot, which has practical applications such as mail delivery and waste collection. We propose a simple iterated variable neighborhood search with an effective