for Journals by Title or ISSN for Articles by Keywords help

Publisher: Springer-Verlag (Total: 2352 journals)

 Artificial Intelligence ReviewJournal Prestige (SJR): 0.833 Citation Impact (citeScore): 4Number of Followers: 18      Hybrid journal (It can contain Open Access articles) ISSN (Print) 1573-7462 - ISSN (Online) 0269-2821 Published by Springer-Verlag  [2352 journals]
• Adjustable autonomy: a systematic literature review
• Authors: Salama A. Mostafa; Mohd Sharifuddin Ahmad; Aida Mustapha
Pages: 149 - 186
Abstract: Developing autonomous systems that operate successfully in dynamic environments entails many challenges. Researchers introduce the concept of adjustable autonomy to mitigate some of these challenges. Adjustable autonomy enables a system to operate in different autonomic conditions and transfers control between the system’s operators. To gauge the extent to which such autonomy has been studied, this paper presents a systematic literature review of adjustable autonomy. It reviews 171 research papers and examines, in detail, 78 research papers. The review provides a fundamental understanding of adjustable autonomy and its application in multi-agent systems. The paper contributes to (1) identifying adjustable autonomy approaches and evaluating their utility, (2) specifying the requirements of formulating adjustable autonomy, (3) presenting adjustable autonomy assessment techniques, and (4) exploring the adjustable autonomy research and identify the research gaps.
PubDate: 2019-02-01
DOI: 10.1007/s10462-017-9560-8
Issue No: Vol. 51, No. 2 (2019)

• A contemporary review of the applications of nature-inspired algorithms
for optimal design of automatic generation control for multi-area power
systems
Pages: 187 - 218
Abstract: The modern electric grid is one the most complex man-made control systems. Proportional–integral–derivative (PID) controllers are widely used in a variety of applications including automatic generation control (AGC), automatic voltage regulators, power system stabilizers and flexible AC transmission system devices. Automatic generation control plays an important role in power system operation to maintain the frequency within an acceptable range and to properly respond to load changes under normal conditions. Using the PIDs, AGC keeps the balance between generation and load demand in order to minimize frequency deviations. Furthermore, the AGC regulates the tie-line power exchange and facilitates bilateral contracts spanning over several control areas, thus ensuring reliable operation of the interconnected transmission system. Since the power system load variations occur continually, generation control is set to automatic to restore the frequency after disturbances. The PID controllers have the advantage of simple structure, good stability, and high reliability. However, a robust and efficient tuning of PID parameters are still being investigated using different techniques. One of the recent areas of such studies is nature-inspired algorithms. The main objective of utilizing nature-inspired algorithms is to optimize parameters of several controllers simultaneously. This paper reviews the latest applications of various nature-inspired algorithms for optimal design of AGC control in power systems. Different algorithms, proposed in the recent literature, are classified based on the type of controller, objective function and test systems.
PubDate: 2019-02-01
DOI: 10.1007/s10462-017-9561-7
Issue No: Vol. 51, No. 2 (2019)

• Differential evolution algorithm with strategy adaptation and
knowledge-based control parameters
• Authors: Qinqin Fan; Weili Wang; Xuefeng Yan
Pages: 219 - 253
Abstract: The search capability of differential evolution (DE) is largely affected by control parameters, mutation and crossover strategies. Therefore, choosing appropriate strategies and control parameters to solve different types of optimization problems or adapt distinct evolution phases is an important and challenging task. To achieve this objective, a DE with strategy adaptation and knowledge-based control parameters (SAKPDE) is proposed in the current study. In the proposed algorithm, a learning–forgetting mechanism is used to implement the adaptation of mutation and crossover strategies. Meanwhile, prior knowledge and opposition learning are utilized to supervise and guide the evolution of control parameters during the entire evolutionary process. SAKPDE is compared with eight improved DEs and four non-DE evolutionary algorithms using three well-known test suites (i.e., BBOB2012, IEEE CEC2005, and IEEE CEC2014). The results indicate that the average performance of SAKPDE is highly competitive among all compared algorithms.
PubDate: 2019-02-01
DOI: 10.1007/s10462-017-9562-6
Issue No: Vol. 51, No. 2 (2019)

• A study on software fault prediction techniques
• Authors: Santosh S. Rathore; Sandeep Kumar
Pages: 255 - 327
Abstract: Software fault prediction aims to identify fault-prone software modules by using some underlying properties of the software project before the actual testing process begins. It helps in obtaining desired software quality with optimized cost and effort. Initially, this paper provides an overview of the software fault prediction process. Next, different dimensions of software fault prediction process are explored and discussed. This review aims to help with the understanding of various elements associated with fault prediction process and to explore various issues involved in the software fault prediction. We search through various digital libraries and identify all the relevant papers published since 1993. The review of these papers are grouped into three classes: software metrics, fault prediction techniques, and data quality issues. For each of the class, taxonomical classification of different techniques and our observations have also been presented. The review and summarization in the tabular form are also given. At the end of the paper, the statistical analysis, observations, challenges, and future directions of software fault prediction have been discussed.
PubDate: 2019-02-01
DOI: 10.1007/s10462-017-9563-5
Issue No: Vol. 51, No. 2 (2019)

• Fall prediction using behavioural modelling from sensor data in smart
homes
• Abstract: The number of methods for identifying potential fall risk is growing as the rate of elderly fallers continues to rise in the UK. Assessments for identifying risk of falling are usually performed in hospitals and other laboratory environments, however these are costly and cause inconvenience for the subject and health services. Replacing these intrusive testing methods with a passive in-home monitoring solution would provide a less time-consuming and cheaper alternative. As sensors become more readily available, machine learning models can be applied to the large amount of data they produce. This can support activity recognition, falls detection, prediction and risk determination. In this review, the growing complexity of sensor data, the required analysis, and the machine learning techniques used to determine risk of falling are explored. The current research on using passive monitoring in the home is discussed, while the viability of active monitoring using vision-based and wearable sensors is considered. Methods of fall detection, prediction and risk determination are then compared.
PubDate: 2019-03-16

• Multi-agent system for microgrids: design, optimization and performance
• Abstract: Smart grids are considered a promising alternative to the existing power grid, combining intelligent energy management with green power generation. Decomposed further into microgrids, these small-scaled power systems increase control and management efficiency. With scattered renewable energy resources and loads, multi-agent systems are a viable tool for controlling and improving the operation of microgrids. They are autonomous systems, where agents interact together to optimize decisions and reach system objectives. This paper presents an overview of multi-agent systems for microgrid control and management. It discusses design elements and performance issues, whereby various performance indicators and optimization algorithms are summarized and compared in terms of convergence time and performance in achieving system objectives. It is found that Particle Swarm Optimization has a good convergence time, so it is combined with other algorithms to address optimization issues in microgrids. Further, information diffusion and consensus algorithms are explored, and according to the literature, many variants of average-consensus algorithm are used to asynchronously reach an equilibrium. Finally, multi-agent system for multi-microgrid service restoration is discussed. Throughout the paper, challenges and research gaps are highlighted in each section as an opportunity for future work.
PubDate: 2019-03-08

• A comprehensive review of type-2 fuzzy Ontology
• Abstract: Ontologies are not only crucial for extending the traditional web into the Semantic Web but also for developing intelligent applications, by converting the raw data into smart data, through semantic enrichment. However, crisp Ontologies are not able to represent fuzzy knowledge which is often encountered in real-world applications. Fuzzy Ontology introduces fuzzy logical rules in Ontology for representing imprecise domain concepts such as darkness, hotness, thickness, creamy etc. in a machine-readable and interoperable format. The performance of fuzzy Ontology decreases with the increase of fuzziness in the domain knowledge. Type-2 fuzzy Ontologies (T2FO) were introduced to represent the domain knowledge where the concepts are either extremely vague or their vagueness increases gradually. The type-2 fuzzy Ontology domain is continuously expanding and there is a need to provide a comprehensive review incorporating the literature of T2FO development approaches, its applications in different domains, reasoners developed for inferencing on type-2 fuzzy Ontology, and evaluation approaches. To perform a comprehensive survey about the T2FO, we used Google Scholar as the main literature research tool to review papers published between 1998 to 2018. We then summarized the published approaches by comparing their features proposed for T2FO development, reasoning or inference, and evaluation approaches. This paper also identifies the domains wherein the past T2FO has been used to develop real-world applications. We conclude this paper by summarizing the previous work, and by identifying the research gaps for investigators.
PubDate: 2019-03-01

• Online AdaBoost-based methods for multiclass problems
• Abstract: Boosting is a technique forged to transform a set of weak classifiers into a strong ensemble. To achieve this, the components are trained with different data samples and the hypotheses are aggregated in order to perform a better prediction. The use of boosting in online environments is a comparatively new activity, inspired by its success in offline environments, which is emerging to meet new demands. One of the challenges is to make the methods handle significant amounts of information taking into account computational constraints. This paper proposes two new online boosting methods: the first aims to perform a better weight distribution of the instances to closely match the behavior of AdaBoost.M1 whereas the second focuses on multiclass problems and is based on AdaBoost.M2. Theoretical arguments were used to demonstrate their convergence and also that both methods retain the main features of their traditional counterparts. In addition, we performed experiments to compare the accuracy as well as the memory usage of the proposed methods against other approaches using 20 well-known datasets. Results suggest that, in many different situations, the proposed algorithms maintain high accuracies, outperforming the other tested methods.
PubDate: 2019-03-01

• Filtering techniques for channel selection in motor imagery EEG
applications: a survey
• Abstract: Brain computer interface (BCI) systems are used in a wide range of applications such as communication, neuro-prosthetic and environmental control for disabled persons using robots and manipulators. A typical BCI system uses different types of inputs; however, Electroencephalography (EEG) signals are most widely used due to their non-invasive EEG electrodes, portability, and cost efficiency. The signals generated by the brain while performing or imagining a motor related task [motor imagery (MI)] signals are one of the important inputs for BCI applications. EEG data is usually recorded from more than 100 locations across the brain, so efficient channel selection algorithms are of great importance to identify optimal channels related to a particular application. The main purpose of applying channel selection is to reduce computational complexity while analysing EEG signals, improve classification accuracy by reducing over-fitting, and decrease setup time. Different channel selection evaluation algorithms such as filtering, wrapper, and hybrid methods have been used for extracting optimal channel subsets by using predefined criteria. After extensively reviewing the literature in the field of EEG channel selection, we can conclude that channel selection algorithms provide a possibility to work with fewer channels without affecting the classification accuracy. In some cases, channel selection increases the system performance by removing the noisy channels. The research in the literature shows that the same performance can be achieved using a smaller channel set, with 10–30 channels in most cases. In this paper, we present a survey of recent development in filtering channel selection techniques along with their feature extraction and classification methods for MI-based EEG applications.
PubDate: 2019-02-28

• An enhanced colliding bodies optimization and its application
• Authors: Debao Chen; Renquan Lu; Suwen Li; Feng Zou; Yajun Liu
Abstract: Colliding bodies optimization (CBO) is a recently proposed algorithm, and there are no algorithm-specific parameters that should be previously determined in updating equations of bodies. CBO has been used to solve various optimization problems because of its simple structure. However, CBO suffers from low convergence speed and premature convergence. To enhance CBO’s performance, a new variant named learning strategy based colliding bodies optimization (LSCBO), which is based on the learning strategy of the Teaching–learning-based optimization algorithm (TLBO), is proposed in this paper. In this method, a hybrid strategy combining the colliding process of CBO and the learning process of TLBO is proposed to generate new positions of the bodies. Compared with some other CBO variants, the guidance of the best individual is introduced to improve the convergence speed of CBO, and a random mutation method based on the historic information is designed to help bodies escape from local optima. Moreover, a new method for determining the mass of bodies is designed to avoid computation overflow. To evaluate the effectiveness of LSCBO, 47 benchmark functions and three real-world structural design problems are tested in the simulation experiments, and the results are compared with those of other well-known meta-heuristic algorithms. The statistical simulation results indicate that the performance of CBO is obviously improved by the developed method.
PubDate: 2019-02-14
DOI: 10.1007/s10462-019-09691-x

• Arabic named entity recognition via deep co-learning
Abstract: Named entity recognition (NER) is an important natural language processing (NLP) task with many applications. We tackle the problem of Arabic NER using deep learning based on Arabic word embeddings that capture syntactic and semantic relationships between words. Deep learning has been shown to perform significantly better than other approaches for various NLP tasks including NER. However, deep-learning models also require a significantly large amount of training data, which is highly lacking in the case of the Arabic language. To remedy this, we adopt the semi-supervised co-training approach to the realm of deep learning, which we refer to as deep co-learning. Our deep co-learning approach makes use of a small amount of labeled data, which is augmented with partially labeled data that is automatically generated from Wikipedia. Our approach relies only on word embeddings as features and does not involve any additional feature engineering. Nonetheless, when tested on three different Arabic NER benchmarks, our approach consistently outperforms state-of-the-art Arabic NER approaches, including ones that employ carefully-crafted NLP features. It also consistently outperforms various baselines including purely-supervised deep-learning approaches as well as semi-supervised ones that make use of only unlabeled data such as self-learning and the traditional co-training approach.
PubDate: 2019-02-07
DOI: 10.1007/s10462-019-09688-6

• Covering based multigranulation fuzzy rough sets and corresponding
applications
• Authors: Jianming Zhan; Xiaohong Zhang; Yiyu Yao
Abstract: By combining covering based rough sets, fuzzy rough sets, and multigranulation rough sets, we introduce covering based multigranulation fuzzy rough set models by means of fuzzy $$\beta$$ -neighborhoods. We investigate axiomatic characterizations of covering based optimistic, pessimistic and variable precision multigranulation fuzzy rough set models. We propose coverings based $$\alpha$$ -optimistic (pessimistic) multigranulation fuzzy rough sets and D-optimistic (pessimistic) multigranulation fuzzy rough sets from fuzzy measures. We examine the relationships among these kinds of coverings based fuzzy rough sets. Finally, we apply the proposed models to solve problems for multi-criteria group decision-making.
PubDate: 2019-02-07
DOI: 10.1007/s10462-019-09690-y

• Can autism be catered with artificial intelligence-assisted intervention
technology' A comprehensive survey
• Authors: Muhammad Shoaib Jaliaawala; Rizwan Ahmed Khan
Abstract: This article presents an extensive literature review of technology based intervention methodologies for individuals facing autism spectrum disorder (ASD). Reviewed methodologies include: contemporary computer aided systems, computer vision assisted technologies and virtual reality (VR) or artificial intelligence (AI)-assisted interventions. The research over the past decade has provided enough demonstrations that individuals with ASD have a strong interest in technology based interventions, which are useful in both, clinical settings as well as at home and classrooms. Despite showing great promise, research in developing an advanced technology based intervention that is clinically quantitative for ASD is minimal. Moreover, the clinicians are generally not convinced about the potential of the technology based interventions due to non-empirical nature of published results. A major reason behind this lack of acceptability is that a vast majority of studies on distinct intervention methodologies do not follow any specific standard or research design. We conclude from our findings that there remains a gap between the research community of computer science, psychology and neuroscience to develop an AI assisted intervention technology for individuals suffering from ASD. Following the development of a standardized AI based intervention technology, a database needs to be developed, to devise effective AI algorithms.
PubDate: 2019-02-02
DOI: 10.1007/s10462-019-09686-8

• The state of the art and taxonomy of big data analytics: view from new big
data framework
• Authors: Azlinah Mohamed; Maryam Khanian Najafabadi; Yap Bee Wah; Ezzatul Akmal Kamaru Zaman; Ruhaila Maskat
Abstract: Big data has become a significant research area due to the birth of enormous data generated from various sources like social media, internet of things and multimedia applications. Big data has played critical role in many decision makings and forecasting domains such as recommendation systems, business analysis, healthcare, web display advertising, clinicians, transportation, fraud detection and tourism marketing. The rapid development of various big data tools such as Hadoop, Storm, Spark, Flink, Kafka and Pig in research and industrial communities has allowed the huge number of data to be distributed, communicated and processed. Big data applications use big data analytics techniques to efficiently analyze large amounts of data. However, choosing the suitable big data tools based on batch and stream data processing and analytics techniques for development a big data system are difficult due to the challenges in processing and applying big data. Practitioners and researchers who are developing big data systems have inadequate information about the current technology and requirement concerning the big data platform. Hence, the strengths and weaknesses of big data technologies and effective solutions for Big Data challenges are needed to be discussed. Hence, due to that, this paper presents a review of the literature that analyzes the use of big data tools and big data analytics techniques in areas like health and medical care, social networking and internet, government and public sector, natural resource management, economic and business sector. The goals of this paper are to (1) understand the trend of big data-related research and current frames of big data technologies; (2) identify trends in the use or research of big data tools based on batch and stream processing and big data analytics techniques; (3) assist and provide new researchers and practitioners to place new research activity in this domain appropriately. The findings of this study will provide insights and knowledge on the existing big data platforms and their application domains, the advantages and disadvantages of big data tools, big data analytics techniques and their use, and new research opportunities in future development of big data systems.
PubDate: 2019-02-01
DOI: 10.1007/s10462-019-09685-9

• A meta-heuristic proposal for inverse kinematics solution of 7-DOF serial
robotic manipulator: quantum behaved particle swarm algorithm
• Authors: Serkan Dereli; Raşit Köker
Abstract: In this study, a quantum behaved particle swarm algorithm has used for inverse kinematic solution of a 7-degree-of-freedom serial manipulator and the results have been compared with other swarm techniques such as firefly algorithm (FA), particle swarm optimization (PSO) and artificial bee colony (ABC). Firstly, the DH parameters of the robot manipulator are created and transformation matrices are revealed. Afterward, the position equations are derived from these matrices. The position of the end effector of the robotic manipulator in the work space is estimated using Quantum PSO and other swarm algorithms. For this reason, a fitness function which name is Euclidian has been determined. This function calculates the difference between the actual position and the estimated position of the manipulator end effector. In this study, the algorithms have tested with two different scenarios. In the first scenario, values for a single position were obtained while values for a hundred different positions were obtained in the second scenario. In fact, the second scenario confirms the quality of the QPSO in the inverse kinematic solution by verifying the first scenario. According to the results obtained; Quantum behaved PSO has yielded results that are much more efficient than standard PSO, ABC and FA. The advantages of the improved algorithm are the short computation time, fewer iterations and the number of particles.
PubDate: 2019-01-30
DOI: 10.1007/s10462-019-09683-x

• A study on features of social recommender systems
• Authors: Jyoti Shokeen; Chhavi Rana
Abstract: Recommender system is an emerging field of research with the advent of World Wide Web and E-commerce. Recently, an increasing usage of social networking websites plausibly has a great impact on diverse facets of our lives in different ways. Initially, researchers used to consider recommender system and social networks as independent topics. With the passage of time, they realized the importance of merging the two to produce enhanced recommendations. The integration of recommender system with social networks produces a new system termed as social recommender system. In this study, we initially describe the concept of recommender system and social recommender system and then investigates different features of social networks that play a major role in generating effective recommendations. Each feature plays an essential role in giving good recommendations and resolving the issues of traditional recommender systems. Lastly, this paper also discusses future work in this area that can aid in enriching the quality of social recommender systems.
PubDate: 2019-01-29
DOI: 10.1007/s10462-019-09684-w

• A review of unsupervised feature selection methods
• Authors: Saúl Solorio-Fernández; J. Ariel Carrasco-Ochoa; José Fco. Martínez-Trinidad
Abstract: In recent years, unsupervised feature selection methods have raised considerable interest in many research areas; this is mainly due to their ability to identify and select relevant features without needing class label information. In this paper, we provide a comprehensive and structured review of the most relevant and recent unsupervised feature selection methods reported in the literature. We present a taxonomy of these methods and describe the main characteristics and the fundamental ideas they are based on. Additionally, we summarized the advantages and disadvantages of the general lines in which we have categorized the methods analyzed in this review. Moreover, an experimental comparison among the most representative methods of each approach is also presented. Finally, we discuss some important open challenges in this research area.
PubDate: 2019-01-29
DOI: 10.1007/s10462-019-09682-y

• Evaluating the websites of academic departments through SEO criteria: a
hesitant fuzzy linguistic MCDM approach
• Authors: Barış Özkan; Eren Özceylan; Mehmet Kabak; Metin Dağdeviren
Abstract: Search Engine Optimization (SEO) is the process of managing web content in a manner that elevates page rankings in search engines. Among other sectors, academic world is one of the number-one categories for search based on the percentage of web traffic generated through search engine referrals. However, SEO includes a number of factors grouped into two as ‘on page’ and ‘off page.’ To obtain maximum benefit from SEO, relevant factors/criteria should be considered using multi-criteria decision making (MCDM) methods. The focus of this paper is to consider SEO criteria evaluation as a MCDM problem in which the criteria are in different priority levels and the criteria values take the form of hesitant fuzzy linguistic term sets to facilitate the elicitation of information in hesitate situations. A three-step solution approach is developed: (i) determination of 21 SEO criteria, such as page loading time, page size and meta-keyword (ii) prioritizing the criteria using hesitant fuzzy analytic hierarchy process, and (iii) ranking 70 Turkish websites of the industrial engineering departments using Technique for Order Preference by Similarity to Ideal Solution. The results show that trust flow and XML sitemap are the determinant criteria among others. Using the proposed method, web designers can approach SEO from weighted criteria perspective.
PubDate: 2019-01-29
DOI: 10.1007/s10462-019-09681-z

• Machine Learning and Deep Learning frameworks and libraries for
large-scale data mining: a survey
• Authors: Giang Nguyen; Stefan Dlugolinsky; Martin Bobák; Viet Tran; Álvaro López García; Ignacio Heredia; Peter Malík; Ladislav Hluchý
Abstract: The combined impact of new computing resources and techniques with an increasing avalanche of large datasets, is transforming many research areas and may lead to technological breakthroughs that can be used by billions of people. In the recent years, Machine Learning and especially its subfield Deep Learning have seen impressive advances. Techniques developed within these two fields are now able to analyze and learn from huge amounts of real world examples in a disparate formats. While the number of Machine Learning algorithms is extensive and growing, their implementations through frameworks and libraries is also extensive and growing too. The software development in this field is fast paced with a large number of open-source software coming from the academy, industry, start-ups or wider open-source communities. This survey presents a recent time-slide comprehensive overview with comparisons as well as trends in development and usage of cutting-edge Artificial Intelligence software. It also provides an overview of massive parallelism support that is capable of scaling computation effectively and efficiently in the era of Big Data.
PubDate: 2019-01-19
DOI: 10.1007/s10462-018-09679-z

• Survey on supervised machine learning techniques for automatic text
classification
• Authors: Ammar Ismael Kadhim
Abstract: Supervised machine learning studies are gaining more significant recently because of the availability of the increasing number of the electronic documents from different resources. Text classification can be defined that the task was automatically categorized a group documents into one or more predefined classes according to their subjects. Thereby, the major objective of text classification is to enable users for extracting information from textual resource and deals with process such as retrieval, classification, and machine learning techniques together in order to classify different pattern. In text classification technique, term weighting methods design suitable weights to the specific terms to enhance the text classification performance. This paper surveys of text classification, process of different term weighing methods and comparison between different classification techniques.
PubDate: 2019-01-19
DOI: 10.1007/s10462-018-09677-1

JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
Fax: +00 44 (0)131 4513327

Home (Search)
Subjects A-Z
Publishers A-Z
Customise
APIs