-
Probabilistic linguistic multiple attribute group decision making for location planning of electric vehicle charging stations based on the generalized Dice similarity measures Artif. Intell. Rev. (IF 5.747) Pub Date : 2021-01-16 Guiwu Wei, Cun Wei, Jiang Wu, Yanfeng Guo
The location of the electric vehicle charging station is deemed to be a multiple attribute group decision making (MAGDM) issue involving many experts and many conflicting attributes. In practical MAGDM issues, the information of uncertain and fuzzy cognitive decision is well-depicted by linguistic term sets (LTSs). These LTSs could be simply shifted into the probabilistic linguistic sets (PLTSs). In
-
Advances in Sine Cosine Algorithm: A comprehensive survey Artif. Intell. Rev. (IF 5.747) Pub Date : 2021-01-11 Laith Abualigah, Ali Diabat
The Sine Cosine Algorithm (SCA) is a population-based optimization algorithm introduced by Mirjalili in 2016, motivated by the trigonometric sine and cosine functions. After providing an overview of the SCA algorithm, we survey a number of SCA variants and applications that have appeared in the literature. We then present the results of a series of computational experiments to validate the performance
-
Artificial intelligence-based techniques for analysis of body cavity fluids: a review Artif. Intell. Rev. (IF 5.747) Pub Date : 2021-01-08 Aftab Ahmad Mir, Abid Sarwar
This paper tends to present a systematic review of the applications of Artificial Intelligence (AI) in the field of medical diagnosis vis-a-vis fluid cytology. The study is based on the research articles published in various reputed international and national journals and the conference proceedings from 1990 to till date. AI-based systems are becoming useful as alternative approaches to conventional
-
A quantum-based sine cosine algorithm for solving general systems of nonlinear equations Artif. Intell. Rev. (IF 5.747) Pub Date : 2021-01-08 Rizk M. Rizk-Allah
In this paper, a quantum-based sine cosine algorithm, named as Q-SCA, is proposed for solving general systems of nonlinear equations. The Q-SCA hybridizes the sine cosine algorithm (SCA) with quantum local search (QLS) for enhancing the diversity of solutions and preventing local optima entrapment. The essence of the proposed Q-SCA is to speed up the optimum searching operation and to accelerate the
-
Various dimension reduction techniques for high dimensional data analysis: a review Artif. Intell. Rev. (IF 5.747) Pub Date : 2021-01-08 Papia Ray, S. Surender Reddy, Tuhina Banerjee
In the era of healthcare, and its related research fields, the dimensionality problem of high dimensional data is a massive challenge as it contains a huge number of variables forming complex data matrices. The demand for dimension reduction of complex data is growing immensely to improvise data prediction, analysis and visualization. In general, dimension reduction techniques are defined as a compression
-
A review of q-rung orthopair fuzzy information: bibliometrics and future directions Artif. Intell. Rev. (IF 5.747) Pub Date : 2021-01-08 Xindong Peng, Zhigang Luo
The q-rung orthopair fuzzy set (q-ROFS), initiated by Yager, is a novel tool to dispose of indeterminacy that considers the membership \(\mu\) and non-membership \(\nu\), which satisfy the limited condition \(0\le \mu ^q+\nu ^q\le 1\). It can be employed in characterizing the vague preference more precisely and flexibly than intuitionistic fuzzy set and Pythagorean fuzzy set. q-ROFS has attracted deep
-
QoS-driven metaheuristic service composition schemes: a comprehensive overview Artif. Intell. Rev. (IF 5.747) Pub Date : 2021-01-08 Mohammad Masdari, Mehdi Nouzad, Suat Ozdemir
Services Oriented Architecture provides Web Services (WSs) as reusable software components that can be applied to create more complicate composite services for users according to the specified QoS limitations. However, considering many WSs that may be appropriate for each task of a user-submitted workflow, finding the optimal WSs for a composite WS to maximize the overall QoS is an NP-hard problem
-
Machine learning towards intelligent systems: applications, challenges, and opportunities Artif. Intell. Rev. (IF 5.747) Pub Date : 2021-01-08 MohammadNoor Injadat, Abdallah Moubayed, Ali Bou Nassif, Abdallah Shami
The emergence and continued reliance on the Internet and related technologies has resulted in the generation of large amounts of data that can be made available for analyses. However, humans do not possess the cognitive capabilities to understand such large amounts of data. Machine learning (ML) provides a mechanism for humans to process large amounts of data, gain insights about the behavior of the
-
Multi-criteria decision making approach based on SVTrN Dombi aggregation functions Artif. Intell. Rev. (IF 5.747) Pub Date : 2021-01-05 Chiranjibe Jana, G. Muhiuddin, Madhumangal Pal
The neutrosophic set is constructed for modelling of situations specifically with incomplete, indeterminate and inconsistent information. In the study, Dombi operations have been introduced on two single-valued trapezoidal neutrosophic numbers (SVTrNNs). Here, Dombi operation on SVTrNNs, some new averaging and geometric averaging operators namely SVTrN Domi weighted averaging (SVTrNDWA) operator, SVTrN
-
Implicit and hybrid methods for attribute weighting in multi-attribute decision-making: a review study Artif. Intell. Rev. (IF 5.747) Pub Date : 2021-01-02 Julio Pena, Gonzalo Nápoles, Yamisleydi Salgueiro
Attribute weighting is a task of paramount relevance in multi-attribute decision-making (MADM). Over the years, different approaches have been developed to face this problem. Despite the effort of the community, there is a lack of consensus on which method is the most suitable one for a given problem instance. This paper is the second part of a two-part survey on attribute weighting methods in MADM
-
Deep learning approaches to scene text detection: a comprehensive review Artif. Intell. Rev. (IF 5.747) Pub Date : 2021-01-01 Tauseef Khan, Ram Sarkar, Ayatullah Faruk Mollah
In recent times, text detection in the wild has significantly raised its ability due to tremendous success of deep learning models. Applications of computer vision have emerged and got reshaped in a new way in this booming era of deep learning. In the last decade, research community has witnessed drastic changes in the area of text detection from natural scene images in terms of approach, coverage
-
Hierarchical classification with multi-path selection based on granular computing Artif. Intell. Rev. (IF 5.747) Pub Date : 2020-09-05 Shunxin Guo, Hong Zhao
Hierarchical classification is a research hotspot in machine learning due to the widespread existence of data with hierarchical class structures. Existing hierarchical classification methods based on granular computing can effectively reduce the computational complexity by considering the granularity of classes. However, their predictive accuracy is affected by inter-level error propagation within
-
Persistence codebooks for topological data analysis Artif. Intell. Rev. (IF 5.747) Pub Date : 2020-09-01 Bartosz Zieliński, Michał Lipiński, Mateusz Juda, Matthias Zeppelzauer, Paweł Dłotko
Persistent homology is a rigorous mathematical theory that provides a robust descriptor of data in the form of persistence diagrams (PDs) which are 2D multisets of points. Their variable size makes them, however, difficult to combine with typical machine learning workflows. In this paper we introduce persistence codebooks, a novel expressive and discriminative fixed-size vectorized representation of
-
On the evaluation and combination of state-of-the-art features in Twitter sentiment analysis Artif. Intell. Rev. (IF 5.747) Pub Date : 2020-08-27 Jonnathan Carvalho, Alexandre Plastino
Sentiment analysis of short informal texts, such as tweets, remains a challenging task due to their particular characteristics. Much effort has been made in the literature of Twitter sentiment analysis to achieve an effective and efficient representation of tweets. In this context, distinct types of features have been proposed and employed, from the simple n-gram representation to meta-features to
-
Nature inspired optimization algorithms or simply variations of metaheuristics? Artif. Intell. Rev. (IF 5.747) Pub Date : 2020-08-24 Alexandros Tzanetos, Georgios Dounias
In the last decade, we observe an increasing number of nature-inspired optimization algorithms, with authors often claiming their novelty and their capabilities of acting as powerful optimization techniques. However, a considerable number of these algorithms do not seem to draw inspiration from nature or to incorporate successful tactics, laws, or practices existing in natural systems, while also some
-
A comparative analysis of gradient boosting algorithms Artif. Intell. Rev. (IF 5.747) Pub Date : 2020-08-24 Candice Bentéjac, Anna Csörgő, Gonzalo Martínez-Muñoz
The family of gradient boosting algorithms has been recently extended with several interesting proposals (i.e. XGBoost, LightGBM and CatBoost) that focus on both speed and accuracy. XGBoost is a scalable ensemble technique that has demonstrated to be a reliable and efficient machine learning challenge solver. LightGBM is an accurate model focused on providing extremely fast training performance using
-
Electric Charged Particles Optimization and its application to the optimal design of a circular antenna array Artif. Intell. Rev. (IF 5.747) Pub Date : 2020-08-20 H. R. E. H. Bouchekara
In this paper, a new metaheuristic, Electric Charged Particles Optimization (ECPO) algorithm, is developed. This algorithm is inspired by the interaction (forces exerted) between electric charged particles. It this algorithm not all the particles interact with each other, only selected ones. Then the way they interact with each other is defined by the selected strategy among the three available strategies
-
Joint feature and instance selection using manifold data criteria: application to image classification Artif. Intell. Rev. (IF 5.747) Pub Date : 2020-08-20 Fadi Dornaika
In many pattern recognition applications feature selection and instance selection can be used as two data preprocessing methods that aim at reducing the computational cost of the learning process. Moreover, in some cases, feature subset selection can improve the classification performance. Feature selection and instance selection can be interesting since the choice of features and instances greatly
-
Deep learning techniques for rating prediction: a survey of the state-of-the-art Artif. Intell. Rev. (IF 5.747) Pub Date : 2020-08-19 Zahid Younas Khan, Zhendong Niu, Sulis Sandiwarno, Rukundo Prince
With the growth of online information, varying personalization drifts and volatile behaviors of internet users, recommender systems are effective tools for information filtering to overcome the information overload problem. Recommender systems utilize rating prediction approaches i.e. predicting the rating that a user will give to a particular item, to generate ranked lists of items according to the
-
An association between fingerprint patterns with blood group and lifestyle based diseases: a review. Artif. Intell. Rev. (IF 5.747) Pub Date : 2020-08-18 Vijaykumar Patil,D R Ingle
In the current era of the digital world, the hash of any digital means considered as a footprint or fingerprint of any digital term but from the ancient era, human fingerprint considered as the most trustworthy criteria for identification and it also cannot be changed with time even up to the death of an individual. In the court of law, fingerprint-proof is undeniably the most dependable and acceptable
-
Vision-based robotic grasping from object localization, object pose estimation to grasp estimation for parallel grippers: a review Artif. Intell. Rev. (IF 5.747) Pub Date : 2020-08-17 Guoguang Du, Kai Wang, Shiguo Lian, Kaiyong Zhao
This paper presents a comprehensive survey on vision-based robotic grasping. We conclude three key tasks during vision-based robotic grasping, which are object localization, object pose estimation and grasp estimation. In detail, the object localization task contains object localization without classification, object detection and object instance segmentation. This task provides the regions of the
-
A texture feature based approach for person verification using footprint bio-metric Artif. Intell. Rev. (IF 5.747) Pub Date : 2020-08-09 Riti Kushwaha, Gaurav Singal, Neeta Nain
Biometrics is the study of unique characteristics present in the human body such as fingerprint, palm-print, retina, iris, footprint, etc. While other traits have been explored widely, only a few people have been considered the foot-palm region, despite having unique properties. Prior work has explored the foot shape features using length, width, major axis, minor axis, centroid, etc. but they are
-
Online handwriting recognition systems for Indic and non-Indic scripts: a review Artif. Intell. Rev. (IF 5.747) Pub Date : 2020-08-08 Harjeet Singh, R. K. Sharma, V. P. Singh
Handwriting recognition is one of the challenging tasks in the area of pattern recognition and machine learning. Handwriting recognition has two flavors, namely, Offline Handwriting Recognition and Online Handwriting Recognition. Though, saturation level has been achieved in machine printed (Offline) character recognition. Presently, due to dramatical development in IT sector, touch-based devices are
-
A study on evolutionary computing based web service selection techniques Artif. Intell. Rev. (IF 5.747) Pub Date : 2020-08-07 Lalit Purohit, Sandeep Kumar
Many service providers are offering their business functionality as web services. The problem of web service selection is a complex and time-consuming activity. Among other techniques, a significant work has been reported on the use of evolutionary computing based algorithms in determining optimal web service for a task. A rigorous review of the state-of-the-art for efficient selection of web services
-
Using memetic algorithm for robustness testing of contract-based software models Artif. Intell. Rev. (IF 5.747) Pub Date : 2020-08-06 Anvar Bahrampour, Vahid Rafe
Graph Transformation System (GTS) can formally specify the behavioral aspects of complex systems through graph-based contracts. Test suite generation under normal conditions from GTS specifications is a task well-suited to evolutionary algorithms such as Genetic and Particle Swarm Optimization (PSO) metaheuristics. However, testing the vulnerabilities of a system under unexpected events such as invalid
-
360 degree view of cross-domain opinion classification: a survey Artif. Intell. Rev. (IF 5.747) Pub Date : 2020-08-06 Rahul Kumar Singh, Manoj Kumar Sachan, R. B. Patel
In the field of natural language processing and text mining, sentiment analysis (SA) has received huge attention from various researchers’ across the globe. By the prevalence of Web 2.0, user’s became more vigilant to share, promote and express themselves along with any issues or challenges that are being encountered on daily activities through the Internet (social media, micro-blogs, e-commerce, etc
-
Risk assessment in discrete production processes considering uncertainty and reliability: Z-number multi-stage fuzzy cognitive map with fuzzy learning algorithm Artif. Intell. Rev. (IF 5.747) Pub Date : 2020-08-06 Mohsen Abbaspour Onari, Samuel Yousefi, Mustafa Jahangoshai Rezaee
The Failure Mode and Effects Analysis (FMEA) technique due to its proactive nature can identify failures and their causes as well as potential effects, and provide preventive/controlling measures before they occur. Nevertheless, some of the shortcomings of the FMEA technique like lack of a mental framework for considering the relationships between risks, lack of systematic perspective in confronting
-
Deep learning for biomedical image reconstruction: a survey Artif. Intell. Rev. (IF 5.747) Pub Date : 2020-08-05 Hanene Ben Yedder, Ben Cardoen, Ghassan Hamarneh
Medical imaging is an invaluable resource in medicine as it enables to peer inside the human body and provides scientists and physicians with a wealth of information indispensable for understanding, modelling, diagnosis, and treatment of diseases. Reconstruction algorithms entail transforming signals collected by acquisition hardware into interpretable images. Reconstruction is a challenging task given
-
Interval valued m -polar fuzzy planar graph and its application Artif. Intell. Rev. (IF 5.747) Pub Date : 2020-08-02 Tanmoy Mahapatra, Sankar Sahoo, Ganesh Ghorai, Madhumangal Pal
In this article, a new idea of interval-valued m-polar fuzzy (IVmPF) graph is introduced and investigated some of it’s properties. Here, IVmPF multiset, interval-valued m-polar fuzzy (IVmPF) multi graph are presented. IVmPF planar value of an interval-valued m-polar fuzzy (IVmPF) planar graph along with degree of planarity value is also introduced to measure the planarity value of an interval-valued
-
Evolutionary computation for solving search-based data analytics problems Artif. Intell. Rev. (IF 5.747) Pub Date : 2020-08-01 Shi Cheng, Lianbo Ma, Hui Lu, Xiujuan Lei, Yuhui Shi
Automatic extracting of knowledge from massive data samples, i.e., big data analytics (BDA), has emerged as a vital task in almost all scientific research fields. The BDA problems are rather difficult to solve due to their large-scale, high-dimensional, and dynamic properties, while the problems with small data are usually hard to handle due to insufficient data samples and incomplete information.
-
Major advancements in kernel function approximation Artif. Intell. Rev. (IF 5.747) Pub Date : 2020-08-01 Deena P. Francis, Kumudha Raimond
Kernel based methods have become popular in a wide variety of machine learning tasks. They rely on the computation of kernel functions, which implicitly transform the data in its input space to data in a very high dimensional space. Efficient application of these functions have been subject to study in the last 10 years. The main focus was on improving the scalability of kernel based methods. In this
-
Artificial intelligence based on fuzzy logic for the analysis of human movement in healthy people: a systematic review Artif. Intell. Rev. (IF 5.747) Pub Date : 2020-08-01 Bráulio Nascimento Lima, Pietro Balducci, Ricardo Pablo Passos, Claudio Novelli, Carlos Henrique Prevital Fileni, Fábio Vieira, Leandro Borelli de Camargo, Guanis de Barros Vilela Junior
Technological advances that involve computing and artificial intelligence (AI) have led to advances in analysis methods. Fuzzy logic (FL) serves as a qualitative interpretation tool for AI. The objective of this systematic review is to investigate the methods of human movement (HM) analysis using AI through FL to understand the characteristics of the movement of healthy people. To identify relevant
-
Multi-dimensional Bayesian network classifiers: A survey Artif. Intell. Rev. (IF 5.747) Pub Date : 2020-07-11 Santiago Gil-Begue, Concha Bielza, Pedro Larrañaga
Multi-dimensional classification is a cutting-edge problem, in which the values of multiple class variables have to be simultaneously assigned to a given example. It is an extension of the well known multi-label subproblem, in which the class variables are all binary. In this article, we review and expand the set of performance evaluation measures suitable for assessing multi-dimensional classifiers
-
Dynamic uncertain causality graph for computer-aided general clinical diagnoses with nasal obstruction as an illustration Artif. Intell. Rev. (IF 5.747) Pub Date : 2020-07-10 Qin Zhang, Xusong Bu, Mingxia Zhang, Zhan Zhang, Jie Hu
Many AI systems have been developed for clinical diagnoses, in which most of them lack interpretability in both knowledge representation and inference results. The newly developed Dynamic Uncertain Causality Graph (DUCG) is a probabilistic graphical model with strong interpretability. However, existing DUCG is mainly for fault diagnoses of large, complex industrial systems. In this paper, we extend
-
A survey of sentiment analysis in the Portuguese language Artif. Intell. Rev. (IF 5.747) Pub Date : 2020-07-06 Denilson Alves Pereira
Sentiment analysis is an area of study that aims to develop computational methods and tools to extract and classify the opinions and emotions expressed by people on social networks, blogs, forums, online shoppings, and others. A lot of research has been developed addressing opinions expressed in the English language. However, studies involving the Portuguese language still need to be advanced to make
-
Bio-inspired VANET routing optimization: an overview Artif. Intell. Rev. (IF 5.747) Pub Date : 2020-07-06 Youcef Azzoug, Abdelmadjid Boukra
This paper demonstrates a recapitulated historic evolution further to a future overview of all vehicular ad-hoc network (VANET) routing problems that concern either directly related routing tasks or targeting a set of diverse routing-related techniques with the aid of the bio-inspired approaches. In this lecture, we serialize, in a synchronous observation, the evolution and tendencies of the VANET
-
Dıscrete socıal spıder algorıthm for the travelıng salesman problem Artif. Intell. Rev. (IF 5.747) Pub Date : 2020-06-30 Emine BAŞ, Erkan ÜLKER
Heuristic algorithms are often used to find solutions to real complex world problems. These algorithms can provide solutions close to the global optimum at an acceptable time for optimization problems. Social Spider Algorithm (SSA) is one of the newly proposed heuristic algorithms and based on the behavior of the spider. Firstly it has been proposed to solve the continuous optimization problems. In
-
Metaheuristic-based adaptive curriculum sequencing approaches: a systematic review and mapping of the literature Artif. Intell. Rev. (IF 5.747) Pub Date : 2020-06-27 Marcelo de Oliveira Costa Machado, Natalie Ferraz Silva Bravo, André Ferreira Martins, Heder Soares Bernardino, Eduardo Barrere, Jairo Francisco de Souza
The presentation of learning materials in a sequence, which considers the association of students’ individual characteristics with those of the knowledge domain of interest, is an effective learning strategy in online learning systems, especially if related to traditional approaches. However, this sequencing, called Adaptive Curriculum Sequencing (ACS), represents a problem that falls in the NP-Hard
-
Deep learning techniques for skin lesion analysis and melanoma cancer detection: a survey of state-of-the-art Artif. Intell. Rev. (IF 5.747) Pub Date : 2020-06-27 Adekanmi Adegun, Serestina Viriri
Analysis of skin lesion images via visual inspection and manual examination to diagnose skin cancer has always been cumbersome. This manual examination of skin lesions in order to detect melanoma can be time-consuming and tedious. With the advancement in technology and rapid increase in computational resources, various machine learning techniques and deep learning models have emerged for the analysis
-
Survey on evaluation methods for dialogue systems Artif. Intell. Rev. (IF 5.747) Pub Date : 2020-06-25 Jan Deriu, Alvaro Rodrigo, Arantxa Otegi, Guillermo Echegoyen, Sophie Rosset, Eneko Agirre, Mark Cieliebak
In this paper, we survey the methods and concepts developed for the evaluation of dialogue systems. Evaluation, in and of itself, is a crucial part during the development process. Often, dialogue systems are evaluated by means of human evaluations and questionnaires. However, this tends to be very cost- and time-intensive. Thus, much work has been put into finding methods which allow a reduction in
-
Improving coalition structure search with an imperfect algorithm: analysis and evaluation results Artif. Intell. Rev. (IF 5.747) Pub Date : 2020-06-23 Narayan Changder, Samir Aknine, Animesh Dutta
Optimal Coalition Structure Generation (CSG) is a significant research problem in multi-agent systems that remains difficult to solve. This problem has many important applications in transportation, eCommerce, distributed sensor networks and others. The CSG problem is NP-complete and finding the optimal result for n agents needs to check \(O (n^n)\) possible partitions. The ODP–IP algorithm (Michalak
-
Chaos Game Optimization: a novel metaheuristic algorithm Artif. Intell. Rev. (IF 5.747) Pub Date : 2020-06-22 Siamak Talatahari, Mahdi Azizi
In this paper, a novel metaheuristic algorithm called Chaos Game Optimization (CGO) is developed for solving optimization problems. The main concept of the CGO algorithm is based on some principles of chaos theory in which the configuration of fractals by chaos game concept and the fractals self-similarity issues are in perspective. A total number of 239 mathematical functions which are categorized
-
Deep learning approach for facial age classification: a survey of the state-of-the-art Artif. Intell. Rev. (IF 5.747) Pub Date : 2020-06-19 Olatunbosun Agbo-Ajala, Serestina Viriri
Age estimation using face images is an exciting and challenging task. The traits from the face images are used to determine age, gender, ethnic background, and emotion of people. Among this set of traits, age estimation can be valuable in several potential real-time applications. The traditional hand-crafted methods relied-on for age estimation, cannot correctly estimate the age. The availability of
-
A robust extension of VIKOR method for bipolar fuzzy sets using connection numbers of SPA theory based metric spaces Artif. Intell. Rev. (IF 5.747) Pub Date : 2020-06-19 Muhammad Riaz, Syeda Tayyba Tehrim
The purpose of this study is to introduce an innovative multi-attribute group decision making (MAGDM) based on bipolar fuzzy set (BFS) by unifying“ VIseKriterijumska Optimizacija I Kompromisno Rasenje (VIKOR)” method. The VIKOR method is considered to be a useful MAGDM method, specifically in conditions where an expert is unable to determine his choice correctly at the initiation of designing a system
-
Dual generalized Bonferroni mean operators based on 2-dimensional uncertain linguistic information and their applications in multi-attribute decision making Artif. Intell. Rev. (IF 5.747) Pub Date : 2020-06-19 Peide Liu, Weiqiao Liu
The dual generalized Bonferroni mean operator is a further extension of the generalized Bonferroni mean operator which can take the interrelationship of different numbers of attributes into account by changing the embedded parameter. The 2-dimensional uncertain linguistic variable (2DULV) adds a second dimensional uncertain linguistic variable (ULV) to express the reliability of the assessment information
-
Intuitionistic 2-tuple linguistic aggregation information based on Einstein operations and their applications in group decision making Artif. Intell. Rev. (IF 5.747) Pub Date : 2020-06-16 Shahzad Faizi; Shoaib Nawaz; Attique Ur-Rehman
The linguistic information can be expressed as a 2-tuple of a linguistic variable and a real number in an interval \([-\frac{1}{2}, \frac{1}{2})\). The intuitionistic 2-tuple linguistic (I2TL) set accurately deals with the imprecise and unpredictable information in those decision-making problems where experts prefer the degree of membership and non-membership values in the form of 2-tuple. The existing
-
Deep semantic segmentation of natural and medical images: a review Artif. Intell. Rev. (IF 5.747) Pub Date : 2020-06-13 Saeid Asgari Taghanaki, Kumar Abhishek, Joseph Paul Cohen, Julien Cohen-Adad, Ghassan Hamarneh
The semantic image segmentation task consists of classifying each pixel of an image into an instance, where each instance corresponds to a class. This task is a part of the concept of scene understanding or better explaining the global context of an image. In the medical image analysis domain, image segmentation can be used for image-guided interventions, radiotherapy, or improved radiological diagnostics
-
A hybrid Harris Hawks optimization algorithm with simulated annealing for feature selection Artif. Intell. Rev. (IF 5.747) Pub Date : 2020-06-13 Mohamed Abdel-Basset, Weiping Ding, Doaa El-Shahat
The significant growth of modern technology and smart systems has left a massive production of big data. Not only are the dimensional problems that face the big data, but there are also other emerging problems such as redundancy, irrelevance, or noise of the features. Therefore, feature selection (FS) has become an urgent need to search for the optimal subset of features. This paper presents a hybrid
-
CovidSens: a vision on reliable social sensing for COVID-19. Artif. Intell. Rev. (IF 5.747) Pub Date : 2020-06-12 Md Tahmid Rashid,Dong Wang
With the spiraling pandemic of the Coronavirus Disease 2019 (COVID-19), it has becoming inherently important to disseminate accurate and timely information about the disease. Due to the ubiquity of Internet connectivity and smart devices, social sensing is emerging as a dynamic AI-driven sensing paradigm to extract real-time observations from online users. In this paper, we propose CovidSens, a vision
-
Projection wavelet weighted twin support vector regression for OFDM system channel estimation Artif. Intell. Rev. (IF 5.747) Pub Date : 2020-06-10 Lidong Wang, Yimei Ma, Xudong Chang, Chuang Gao, Qiang Qu, Xuebo Chen
In this paper, an efficient projection wavelet weighted twin support vector regression (PWWTSVR) based orthogonal frequency division multiplexing system (OFDM) system channel estimation algorithm is proposed. Most Channel estimation algorithms for OFDM systems are based on the linear assumption of channel model. In the proposed algorithm, the OFDM system channel is consumed to be nonlinear and fading
-
A systematic literature review of multicriteria recommender systems Artif. Intell. Rev. (IF 5.747) Pub Date : 2020-06-09 Diego Monti, Giuseppe Rizzo, Maurizio Morisio
Since the first years of the 90s, recommender systems have emerged as effective tools for automatically selecting items according to user preferences. Traditional recommenders rely on the relevance assessments that users express using a single rating for each item. However, some authors started to suggest that this approach could be limited, as we naturally tend to formulate different judgments according
-
Non-goal oriented dialogue agents: state of the art, dataset, and evaluation Artif. Intell. Rev. (IF 5.747) Pub Date : 2020-06-05 Akanksha Mehndiratta, Krishna Asawa
Dialogue agent, a derivative of intelligent agent in the field of computational linguistics, is a computer program that is capable of generating responses and performing conversation in natural language. The field of computational linguistics is flourishing due to the intensive growth of dialogue agents; the most potential one is providing voice controlled smart personal assistant service for handsets
-
CHIRPS: Explaining random forest classification Artif. Intell. Rev. (IF 5.747) Pub Date : 2020-06-04 Julian Hatwell, Mohamed Medhat Gaber, R. Muhammad Atif Azad
Modern machine learning methods typically produce “black box” models that are opaque to interpretation. Yet, their demand has been increasing in the Human-in-the-Loop processes, that is, those processes that require a human agent to verify, approve or reason about the automated decisions before they can be applied. To facilitate this interpretation, we propose Collection of High Importance Random Path
-
Sentiment analysis with deep neural networks: comparative study and performance assessment Artif. Intell. Rev. (IF 5.747) Pub Date : 2020-05-22 Ramesh Wadawadagi, Veerappa Pagi
The current decade has witnessed the remarkable developments in the field of artificial intelligence, and the revolution of deep learning has transformed the whole artificial intelligence industry. Eventually, deep learning techniques have become essential components of any model in today’s computational world. Nevertheless, deep learning techniques promise a high degree of automation with generalized
-
Novel classes of coverings based multigranulation fuzzy rough sets and corresponding applications to multiple attribute group decision-making Artif. Intell. Rev. (IF 5.747) Pub Date : 2020-05-19 Xueling Ma, Jianming Zhan, Bingzhen Sun, José Carlos R. Alcantud
The notion of covering based multigranulation fuzzy rough set (CMGFRS) models is a generalization of both granular computing and covering based fuzzy rough sets. Therefore it has become a powerful tool for coping with vague and multigranular information in cognition. In this paper we introduce three kinds of CMGFRS models by means of fuzzy β-neighborhoods and fuzzy complementary β-neighborhoods, and
-
Soft dominance based multigranulation decision theoretic rough sets and their applications in conflict problems Artif. Intell. Rev. (IF 5.747) Pub Date : 2020-05-18 Noor Rehman, Abbas Ali, Kostaq Hila
The extension of rough set model is a crucial and vast research direction in rough set theory. Meanwhile decision making can be considered as a mental process in which human beings make a choice among several alternatives. However, with the increasing complexity of real decision making problems, the decision makers frequently face the challenge of characterizing their preferences in an uncertain context
-
Neural networks for online learning of non-stationary data streams: a review and application for smart grids flexibility improvement Artif. Intell. Rev. (IF 5.747) Pub Date : 2020-05-17 Zeineb Hammami, Moamar Sayed-Mouchaweh, Wiem Mouelhi, Lamjed Ben Said
Learning efficient predictive models in dynamic environments requires taking into account the continuous changing nature of phenomena generating the data streams, known in machine learning as “concept drift”. Such changes may affect models’ effectiveness over time, requiring permanent updates of parameters and structure to maintain performance. Several supervised machine learning methods have been
-
Selective attention to historical comparison or social comparison in the evolutionary iterated prisoner’s dilemma game Artif. Intell. Rev. (IF 5.747) Pub Date : 2020-05-16 Weijun Zeng, Minqiang Li
This paper investigates an evolutionary iterated prisoner’s dilemma (IPD) model of multiple agents, in which agents interact in terms of the pair-wise IPD game while adapting their attitudes towards income stream risk. Specifically, agents will become more risk averse (or more risk seeking) if their game payoffs exceed (or fall below) their expectations. In particular, agents use their peers’ average
-
A review on the long short-term memory model Artif. Intell. Rev. (IF 5.747) Pub Date : 2020-05-13 Greg Van Houdt, Carlos Mosquera, Gonzalo Nápoles
Long short-term memory (LSTM) has transformed both machine learning and neurocomputing fields. According to several online sources, this model has improved Google’s speech recognition, greatly improved machine translations on Google Translate, and the answers of Amazon’s Alexa. This neural system is also employed by Facebook, reaching over 4 billion LSTM-based translations per day as of 2017. Interestingly
-
Machine learning in medicinal plants recognition: a review Artif. Intell. Rev. (IF 5.747) Pub Date : 2020-05-12 Kalananthni Pushpanathan, Marsyita Hanafi, Syamsiah Mashohor, Wan Fazilah Fazlil Ilahi
Medicinal plants are gaining attention in the pharmaceutical industry due to having less harmful effects reactions and cheaper than modern medicine. Based on these facts, many researchers have shown considerable interest in the research of automatic medicinal plants recognition. There are various opportunities for advancement in producing a robust classifier that has the ability to classify medicinal
Contents have been reproduced by permission of the publishers.