Publisher: Springer-Verlag (Total: 2626 journals)

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 Artificial Intelligence ReviewJournal Prestige (SJR): 0.833 Citation Impact (citeScore): 4Number of Followers: 25      Hybrid journal (It can contain Open Access articles) ISSN (Print) 1573-7462 - ISSN (Online) 0269-2821 Published by Springer-Verlag  [2626 journals]
• Dıscrete socıal spıder algorıthm for the
travelıng salesman problem
• Abstract: Abstract 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 this paper, SSA is rearranged to solve discrete optimization problems. Discrete Social Spider Algorithm (DSSA) is developed by adding explorer spiders and novice spiders in discrete search space. Thus, DSSA's exploration and exploitation capabilities are increased. The performance of the proposed DSSA is investigated on traveling salesman benchmark problems. The Traveling Salesman Problem (TSP) is one of the standard test problems used in the performance analysis of discrete optimization algorithms. DSSA has been tested on a low, middle, and large-scale thirty-eight TSP benchmark datasets. Also, DSSA is compared to eighteen well-known algorithms in the literature. Experimental results show that the performance of proposed DSSA is especially good for low and middle-scale TSP datasets. DSSA can be used as an alternative discrete algorithm for discrete optimization tasks.
PubDate: 2020-06-30

• Metaheuristic-based adaptive curriculum sequencing approaches: a
systematic review and mapping of the literature
• Abstract: Abstract 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 class of problems given the diversity of sequences that could be chosen from ever-larger repositories of learning materials. Thus, metaheuristics are usually employed to tackle this problem. This study aims to present a systematic review and mapping of the literature to identify, analyze, and classify the published solutions related to the ACS problem addressed by metaheuristics. We considered 61 studies in the mapping and 58 studies in the review from 2005 to 2018. Even though the problem is longstanding, it is still discussed, especially considering new modeling and used metaheuristics. In this sense, we emphasize the use of Swarm Intelligence and Genetic Algorithm. Moreover, we have identified that various parameters were considered for students and knowledge domain modeling, however, few student’s intrinsic parameters have been explored in ACS literature.
PubDate: 2020-06-27

• Deep learning techniques for skin lesion analysis and melanoma cancer
detection: a survey of state-of-the-art
• Abstract: Abstract 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 of medical images most especially the skin lesion images. The results of these models have been impressive, however analysis of skin lesion images with these techniques still experiences some challenges due to the unique and complex features of the skin lesion images. This work presents a comprehensive survey of techniques that have been used for detecting skin cancer from skin lesion images. The paper is aimed to provide an up-to-date survey that will assist investigators in developing efficient models that automatically and accurately detects melanoma from skin lesion images. The paper is presented in five folds: First, we identify the challenges in detecting melanoma from skin lesions. Second, we discuss the pre-processing and segmentation techniques of skin lesion images. Third, we make comparative analysis of the state-of-the-arts. Fourth we discuss classification techniques for classifying skin lesions into different classes of skin cancer. We finally explore and analyse the performance of the state-of-the-arts methods employed in popular skin lesion image analysis competitions and challenges of ISIC 2018 and 2019. Application of ensemble deep learning models on well pre-processed and segmented images results in better classification performance of the skin lesion images.
PubDate: 2020-06-27

• Survey on evaluation methods for dialogue systems
• Abstract: Abstract 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 involvement of human labour. In this survey, we present the main concepts and methods. For this, we differentiate between the various classes of dialogue systems (task-oriented, conversational, and question-answering dialogue systems). We cover each class by introducing the main technologies developed for the dialogue systems and then present the evaluation methods regarding that class.
PubDate: 2020-06-25

• Improving coalition structure search with an imperfect algorithm: analysis
and evaluation results
• Abstract: Abstract 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 et al. in Artif Intell 230:14–50, 2016) achieves the current lowest worst-case time complexity of $$O (3^n)$$. In the light of its high computational time complexity, we devise an Imperfect Dynamic Programming (ImDP) algorithm for the CSG problem with runtime $$O (n2^n)$$ given n agents. Imperfect algorithm means that there are some contrived inputs for which the algorithm fails to give the optimal result. We benchmarked ImDP against ODP–IP and proved its efficiency. Experimental results confirmed that ImDP algorithm performance is better for several data distributions, and for some it improves dramatically ODP–IP. For example, given 27 agents, with ImDP for agent-based uniform distribution time gain is 91% (i.e. 49 min).
PubDate: 2020-06-23

• Chaos Game Optimization: a novel metaheuristic algorithm
• Abstract: Abstract 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 into four different groups are collected to evaluate the overall performance of the presented novel algorithm. In order to evaluate the results of the CGO algorithm, three comparative analysis with different characteristics are conducted. In the first step, six different metaheuristic algorithms are selected from the literature while the minimum, mean and standard deviation values alongside the number of function evaluations for the CGO and these algorithms are calculated and compared. A complete statistical analysis is also conducted in order to provide a valid judgment about the performance of the CGO algorithm. In the second one, the results of the CGO algorithm are compared to some of the recently developed fractal- and chaos-based algorithms. Finally, the performance of the CGO algorithm is compared to some state-of-the-art algorithms in dealing with the state-of-the-art mathematical functions and one of the recent competitions on single objective real-parameter numerical optimization named “CEC 2017” is considered as numerical examples for this purpose. In addition, a computational cost analysis is also conducted for the presented algorithm. The obtained results proved that the CGO is superior compared to the other metaheuristics in most of the cases.
PubDate: 2020-06-22

• A robust extension of VIKOR method for bipolar fuzzy sets using connection
numbers of SPA theory based metric spaces
• Abstract: Abstract 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. The method of VIKOR is suitable for problems containing conflicting attributes, with an assumption that compromising is admissible for conflict decision, the expert wishes a solution very near to the best, and the different alternatives or choices are processed according to all developed attributes. The theory of set pair analysis is a state-of-the-art uncertainty theory which consists of three factors, including “identity degree”, “discrepancy degree”, and “contrary degree” of connection numbers (CNs) and coincidence with many existing theories dealing with vagueness in the given information. Consequently, inspired by this, in the present study, we make an effort to improve the theory of data measurement by introducing some metric spaces using CNs of BFSs. In this research paper, we extend VIKOR method in the context of CNs based metrics, which are obtained form bipolar fuzzy numbers (BFNs). Firstly, we develop CNs of BFNs as well as metric spaces based on CNs. We also discuss some interesting properties of proposed metric spaces. Secondly, we develop VIKOR method using CNs based metrics to handle an MAGDM problem under bipolar fuzzy type information. The predominance of proposed metric spaces is also studied by the means of examples. Furthermore, we demonstrate the efficiency of the extended VIKOR method by solving a numerical example, sensitivity analysis and a detailed comparison with some existing approaches.
PubDate: 2020-06-19

• Dual generalized Bonferroni mean operators based on 2-dimensional
uncertain linguistic information and their applications in multi-attribute
decision making
• Abstract: Abstract 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 in first dimensional information, which is more rational and accurate than the ULV. In this paper, for combining the advantages of them, we propose the dual generalized weighted Bonferroni mean operator for 2DULVs (2DULDGWBM) and the dual generalized weighted Bonferroni geometric mean operator for 2DULVs (2DULDGWBGM). In addition, we explore several particular cases and some rational characters of them. Further, a new approach is introduced to handle multi-attribute decision making problems in the environment of 2DULVs by the proposed operators. Finally, we utilize several illustrative examples to testify the validity and superiority of this new method by comparing with several other methods.
PubDate: 2020-06-19

• Deep learning approach for facial age classification: a survey of the
state-of-the-art
• Abstract: Abstract 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 huge datasets for training and an increase in computational power has made deep learning with convolutional neural network a better method for age estimation; convolutional neural network will learn discriminative feature descriptors directly from image pixels. Several convolutional neural net work approaches have been proposed by many of the researchers, and these have made a significant impact on the results and performances of age estimation systems. In this paper, we present a thorough study of the state-of-the-art deep learning techniques which estimate age from human faces. We discuss the popular convolutional neural network architectures used for age estimation, presents a critical analysis of the performance of some deep learning models on popular facial aging datasets, and study the standard evaluation metrics used for performance evaluations. Finally, we try to analyze the main aspects that can increase the performance of the age estimation system in future.
PubDate: 2020-06-19

• Intuitionistic 2-tuple linguistic aggregation information based on
Einstein operations and their applications in group decision making
• Abstract: Abstract 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 approaches used for the aggregation operations of I2TL sets are extremely complicated. This work aims to develop new aggregation operations for I2TL sets using Einstein operations. We present intuitionistic 2-tuple linguistic Einstein weighted averaging (I2TLEWA), and intuitionistic 2-tuple linguistic Einstein weighted geometric (I2TLEWG) operators. We also discuss their properties and relationship between them. Moreover, we numerically test the feasibility and significance of our proposed operators by solving a multi-criteria group decision making (MCGDM) problem. Finally, we do a comparative analysis with another method to give insights on our designed operators for I2TL sets.
PubDate: 2020-06-16

• Deep semantic segmentation of natural and medical images: a review
• Abstract: Abstract 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. In this review, we categorize the leading deep learning-based medical and non-medical image segmentation solutions into six main groups of deep architectural, data synthesis-based, loss function-based, sequenced models, weakly supervised, and multi-task methods and provide a comprehensive review of the contributions in each of these groups. Further, for each group, we analyze each variant of these groups and discuss the limitations of the current approaches and present potential future research directions for semantic image segmentation.
PubDate: 2020-06-13

• A hybrid Harris Hawks optimization algorithm with simulated annealing for
feature selection
• Abstract: Abstract 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 version of the Harris Hawks Optimization algorithm based on Bitwise operations and Simulated Annealing (HHOBSA) to solve the FS problem for classification purposes using wrapper methods. Two bitwise operations (AND bitwise operation and OR bitwise operation) can randomly transfer the most informative features from the best solution to the others in the populations to raise their qualities. The Simulate Annealing (SA) boosts the performance of the HHOBSA algorithm and helps to flee from the local optima. A standard wrapper method K-nearest neighbors with Euclidean distance metric works as an evaluator for the new solutions. A comparison between HHOBSA and other state-of-the-art algorithms is presented based on 24 standard datasets and 19 artificial datasets and their dimension sizes can reach up to thousands. The artificial datasets help to study the effects of different dimensions of data, noise ratios, and the size of samples on the FS process. We employ several performance measures, including classification accuracy, fitness values, size of selected features, and computational time. We conduct two statistical significance tests of HHOBSA like paired-samples T and Wilcoxon signed ranks. The proposed algorithm presented superior results compared to other algorithms.
PubDate: 2020-06-13

• CovidSens: a vision on reliable social sensing for COVID-19
PubDate: 2020-06-12

• Projection wavelet weighted twin support vector regression for OFDM system
channel estimation
• Abstract: Abstract 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 in both time and frequency domains. The PWWTSVR utilizes pilot signals to estimate response of nonlinear wireless channel, which is the main work area of SVR. Projection axis in optimal objective function of PWWRSVR is sought to minimize the variance of the projected points due to the utilization of a priori information of training data. Different from traditional support vector regression algorithm, training samples in different positions in the proposed PWWTSVR model are given different penalty weights determined by the wavelet transform. The weights are applied to both the quadratic empirical risk term and the first-degree empirical risk term to reduce the influence of outliers. The final regressor can avoid the overfitting problem to a certain extent and yield great generalization ability for channel estimation. The results of numerical experiments show that the propose algorithm has better performance compared to the conventional pilot-aided channel estimation methods.
PubDate: 2020-06-10

• A systematic literature review of multicriteria recommender systems
• Abstract: Abstract 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 to multiple criteria. During the last decade, several studies introduced novel recommender systems capable of exploiting user preferences expressed over multiple criteria. This work proposes a systematic literature review in the field of multicriteria recommender systems. Following a replicable protocol, we selected a total number of 93 studies dealing with this topic. We subsequently analyzed them to provide an answer to five different research questions. We considered what are the most common research problems, recommendation approaches, data mining and machine learning algorithms mentioned in these studies. Furthermore, we investigated the domains of application, the exploited evaluation protocols, metrics and datasets, and the most promising suggestions for future works.
PubDate: 2020-06-09

• Non-goal oriented dialogue agents: state of the art, dataset, and
evaluation
• Abstract: Abstract 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 and homes. The agents are usable, accessible but perform task-related short conversations. Non-goal-oriented dialogue agents are designed to imitate extended human–human conversations, also called as chit-chat, to provide the consumer with a satisfactory experience on the conversation quality. The design of such agents is primarily defined by a language model, unlike goal-oriented dialogue agents that employees slot based or ontology-based frameworks, hence most of the methods are data-driven. This paper surveys the current state of the art of non-goal-oriented dialogue systems specifically data-driven methods, the most prevalent being deep learning. This paper aims at (a) providing an insight of recent methods and architectures proposed for building context and modeling response along with a comprehensive review of the state of the art (b) examine the type of data set and evaluation methods available (c) present the challenges and limitation that the recent models, dataset and evaluation methods constitute.
PubDate: 2020-06-05

• CHIRPS: Explaining random forest classification
• Abstract: Abstract 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 Snippets (CHIRPS); a novel algorithm for explaining random forest classification per data instance. CHIRPS extracts a decision path from each tree in the forest that contributes to the majority classification, and then uses frequent pattern mining to identify the most commonly occurring split conditions. Then a simple, conjunctive form rule is constructed where the antecedent terms are derived from the attributes that had the most influence on the classification. This rule is returned alongside estimates of the rule’s precision and coverage on the training data along with counter-factual details. An experimental study involving nine data sets shows that classification rules returned by CHIRPS have a precision at least as high as the state of the art when evaluated on unseen data (0.91–0.99) and offer a much greater coverage (0.04–0.54). Furthermore, CHIRPS uniquely controls against under- and over-fitting solutions by maximising novel objective functions that are better suited to the local (per instance) explanation setting.
PubDate: 2020-06-04

• A systematic review and meta-analysis on performance of intelligent
systems in lung cancer: Where are we'
• Abstract: Abstract Cancer is one of the human’s life-threatening diseases that does not merely pertain to one organ. Despite the varieties of cancers, lung cancer, with its different growth and spreading mechanisms, can affect the normal cells and disrupt the cell signaling procedure that alters the cell division function. In this systematic review and meta-analysis, the well-known databases are searched based on a Boolean query exclusively for lung cancer and the corresponding artificial intelligent systems. By systematically searching the PubMed and Scopus databases, English-language articles published up to 13 July 2017 were extracted that identify the cancerous and normal cell images using different types of predictive models. Then, the search results will be selected for the pertinent articles encompassing the required information (i.e., inclusion criterion) such as total sample size, true positive, true negative, false positive, and false negative values. The studies without enough information were omitted from further analysis. Considering the lung cancer diagnosis and conducting the meta-analysis on the articles, the results for the improvement trends in the amount of success in the performance of the artificially intelligent systems have been reported. Eventually, two publication bias tests have shown that the possibility of publication bias exists. And, the trends on diagnostic odds ratio and AUC values were immeasurably high, respectively, while those of sensitivity and specificity were moderate.
PubDate: 2020-06-01

• Computerized acoustical techniques for respiratory flow-sound analysis: a
systematic review
• Abstract: Abstract Computerized respiratory sound analysis has recently captured the attention of researchers, and its implementation can assist physicians in the diagnosis of pulmonary pathologies. The relationship between respiratory sounds and breathing flow reveals the pathophysiology of the respiratory system and can be used as a basis for acoustical airflow estimation. Respiratory sound signals are also acoustically analysed for the detection of breath phases. Although this research area is being actively studied, the available literature has not been reviewed. This manuscript highlights the previous studies that focused on the use of computer-based acoustical techniques for the analysis of respiratory sounds and airflow. Articles related to computerized respiratory flow-sound analysis were identified through a search of the Scopus academic database, and 66 articles were ultimately selected for this systematic review. A brief overview of the subject details, auscultation sites, respiratory manoeuvres, sound parameters of interest and techniques used is provided. The findings revealed the following: (1) deterministic relationships can be established between airflow and respiratory sounds, (2) an established strong flow-sound correlation can be used for airflow estimation, (3) breath phase detection and identification without flow measuring devices remains in the infancy research stage and (4) further research needed to examine the potential of computerized respiratory sound analysis in revealing the pathophysiology of airways for future clinical implementation. This review concludes by discussing the possibilities and recommendations for further advancements in computerized acoustical flow-sound analysis.
PubDate: 2020-06-01

• Analyzing rare event, anomaly, novelty and outlier detection terms under
the supervised classification framework
• Abstract: Abstract In recent years, a variety of research areas have contributed to a set of related problems with rare event, anomaly, novelty and outlier detection terms as the main actors. These multiple research areas have created a mix-up between terminology and problems. In some research, similar problems have been named differently; while in some other works, the same term has been used to describe different problems. This confusion between terms and problems causes the repetition of research and hinders the advance of the field. Therefore, a standardization is imperative. The goal of this paper is to underline the differences between each term, and organize the area by looking at all these terms under the umbrella of supervised classification. Therefore, a one-to-one assignment of terms to learning scenarios is proposed. In fact, each learning scenario is associated with the term most frequently used in the literature. In order to validate this proposal, a set of experiments retrieving papers from Google Scholar, ACM Digital Library and IEEE Xplore has been carried out.
PubDate: 2020-06-01

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