Area Of Computational Intelligence Research Articles

Integrating SIFT and CNN Feature Matching for Partial-Duplicate Image Detection

With the increasing popularity of various deep neural networks in the area of computational intelligence, the research attention for content-based image detection/retrieval has been shifted from the handcrafted local features such as scale invariant feature transform (SIFT) to the features derived from convolutional neural networks (CNN). However, the existing image-based CNN features, directly extracted from the entire images, are not suitable for detecting small duplicate regions, while region-based CNN features show limited robustness to a variety of image modifications such as rescaling, occlusion, and noise adding. These will affect the performance of partial-duplicate image detection. To address these issues, we propose an integrated feature matching scheme, which integrates the matching of SIFT features and CNN features between images for partial-duplicate image detection. In this scheme, we first implement SIFT feature matching based on the bag-of-visual-words model to detect the potential duplicate region pairs between images, and then match the CNN features of these regions extracted from the deep convolutional layer of CNN to compute image similarity. Since both the good robustness of SIFT features and the high discriminative power of CNN features are sufficiently explored, our scheme allows an accurate detection. Experimental results show that the proposed approach provides superior accuracy than the state of the arts, while achieves comparable efficiency.

An extensive review of computational intelligence-based optimization algorithms: trends and applications

Area of computational intelligence is gaining researcher’s attention in ongoing trend of technology and evolution due to their high capability to deliver near-optimal solutions. A new hierarchy of algorithms has been proposed in the paper, and they have been organized on the basis of their inspiration sources. The broad two domains of the algorithms are modeling of human mind and nature-inspired intelligence. Nature-inspired computational algorithms being heuristic algorithms are robust and have optimization capability to solve obscure and substantiated problems. The heuristic techniques aim on finding the best possible solution to the query in a satisfiable amount of time. The computational intelligence methods inspired from nature have further been categorized into artificial immune systems, evolutionary algorithms, swarm intelligence, artificial neural networks and geoscience-based algorithms. Geoscience-based domain is the least explored domain in which the algorithms can be developed based on geographic phenomenon taking place on the earth’s surface. An extensive tabular comparison is done among algorithms of all the domains on the basis of various attributes. Also, variants of the algorithms and their implementation in a specific application have been examined. The efficiency and performance of selected algorithms have been compared on clustering and traveling salesman problem for better understanding.

A Bibliometric Overview of the Field of Type-2 Fuzzy Sets and Systems [Discussion Forum

Fuzzy Sets and Systems is an area of computational intelligence, pioneered by Lotfi Zadeh over 50 years ago in a seminal paper in Information and Control. Fuzzy Sets (FSs) deal with uncertainty in our knowledge of a particular situation. Research and applications in FSs have grown steadily over 50 years. More recently, we have seen a growth in Type-2 Fuzzy Set (T2 FS) related papers, where T2 FSs are utilized to handle uncertainty in realworld problems. In this paper, we have used bibliometric methods to obtain a broad overview of the area of T2 FSs. This method analyzes information on the bibliographic details of published journal papers, which includes title, authors, author address, journals and citations, extracted from the Science and Social Science Citation Indices in the Web of Science (WoS) database for the last 20 years (1997-2017). We have compared the growth of publications in the field of FSs, and its subset T2 FSs, identified highly cited papers in T2 FSs, highly cited authors, key institutions, and main countries with researchers involved in T2 FS related research.

Human Intelligence Meets Smart Machine: A Special Event at the IEEE International Conference on Systems, Man, and Cybernetics 2018

Machine learning is a popular approach for cybernetics systems and has always been considered as an important research topic in the computational intelligence area. The Open Go Darkforest (OGD) cloud platform, a machine?human co-learning model, was established to help human players learn and analyze the game Go based on their learning behavior and performance. To evaluate humans' learning progress, a special event—Human and Smart Machine Co-Learning @ IEEE SMC 2018—was held at the IEEE International Conference on Systems, Man, and Cybernetics (SMC) in Miyazaki, Japan, in October 2018.

The Role of Ensemble Learning in Stock Market Classification Model Accuracy Enhancement Based on Naive Bayes Classifiers

Over the last years, methods of hybrid and ensemble have attracted the attention of the data mining community. Moreover, in the computational intelligence area such as machine learning, constructing and adaptive hybrid models have become essential to achieve good performance. However, the accuracy of stock market classification models is still low, and this has negatively affected the stock market indicators. Furthermore, there are many factors that have a direct effect on the classification models’ accuracies which were not addressed by previous research such as the automatic labelling technique which results in low classification accuracy due to the absence of specific lexicon, and the suitability of the classifiers to the data features and domain. In this research, a proposed model is designed to enhance the classification accuracy by the incorporation of stock market domain expert labelling technique and the construction of an ensemble Naïve Bayes classifiers to classify the stock market sentiments. The methodology for this research consists of five phases. The first phase is data collection, and the second phase is labelling, in which polarity of data is specified and negative, positive or neutral values are assigned. The third phase involves data pre-processing. The fourth phase is the classification phase in which suitable patterns of the stock market are identified by Ensemble Naïve Bayes classifiers, and the final is the performance and evaluation. The classification method has produced a significant result; it has achieved accuracy of more than 89%.

PREFACE

It is with great pleasure that I introduce the proceedings of the 2019 High Performance Computing and Computational Intelligence Conference (HPCCI 2019) which was held recently Chengdu, China during October 25-27, 2019. HPCCI 2019 was dedicated to technical issues related to High Performance Computing and Computational Intelligence.High performance computing represents the national strength of a country and plays an essential supporting role in the development of national science and technology. Quantum computing, as a new computing method, could be an emerging area of high-performance computing in the future. The development of high performance computing may have some breakthroughs in these areas. At the same time, it is found that the implicit parallelism provided by the quantum system improves the performance of the algorithm. Quantum theory may be an important research direction to construct the basic theoretical system for some intelligence schemes in the future. The development of computational intelligence cannot be separated from the support of high-performance computing. The development of the two subject areas has gradually formed an integrated system. Based on it, the objectives of the conference were not only to provide opportunities to the international scientists to present state of the art research in High Performance Computing and Computational Intelligence but also to provide a forum to discuss ideas for future directions in the field and explore possibilities for collaborative research projects.The conference program included keynote, oral, and poster presentations from scholars working in the areas of High Performance Computing and Computational Intelligence from all over the world. It covered recent trends and progress made in the field of High Performance Computing and Computational Intelligence. Keynote lectures regarding the latest information in their respective areas of expertise were shared by experts.The proceedings present a selection of high quality papers submitted to the conference by researchers from universities, research institutes, and industry. All papers were subjected to peer-review by conference committee members and international reviewers. The papers were selected based on their quality and their relevance to the conference. The proceedings present recent advances in the fields of High performance Computing and Computing Intelligence.I would like to express special gratitude to members of the conference committee and organizers of the conference. I would also like to thank the reviewers for their valuable time and advice which helped in improving the quality of the papers selected for presentation at the conference and for publication in the proceedings. Finally I want to thank the authors, the members of the organizing committee, the reviewers, the chairpersons, sponsors, and all other conference participants for their support of HPCCI 2019.Conference ChairProf. Peng Wang Southwest Minzu University, China

Explicit Control of Implicit Parallelism in Decomposition-Based Evolutionary Many-Objective Optimization Algorithms [Research Frontier

Over the past two decades, Evolutionary Multi-objective Optimization (EMO) algorithms have demonstrated their ability to find and maintain multiple trade-off solutions in two and three-objective problems, making EMO as one of the most emergent and exciting fields of research and application within Computational Intelligence (CI) area. The main reason for EMO's success is that the population-based EMO operators are able to establish an implicit parallel search within an evolving population to find multiple Pareto-optimal regions of the search space parallelly. For many-objective optimization problems involving a largedimensional objective space, the extent of implicit parallelism is argued here to be too generic, compared to the same in a lower-dimensional objective space. Decomposition-based EMO algorithms - a recent trend in EMO literature - which divide the overall computing task into a number of sub-tasks of focusing within a region of the search space have found to be successful in solving many-objective problems. In this paper, we study the effect of explicit control of an algorithm's implicit parallelism mechanism for achieving an enhanced performance of decomposition-based EMO algorithms. We consider three decomposition-based many-objective evolutionary algorithms (EAs) - MOEA/D, MOEA/D-M2M, and NSGA-III - for this purpose. We also investigate another explicit control strategy of suitably choosing a normalization method of objectives for improving the performance of MOEA/D and MOEA/ D-M2M methods, and report much improved performance than their original counterparts. The principles of this study are valid for any population-based search and optimization algorithms and can be extended to improve the performance other single-objective EA, EMO, and other relevant CI methods.

Evolutionary Fuzzy Systems for Explainable Artificial Intelligence: Why, When, What for, and Where to?

Evolutionary fuzzy systems are one of the greatest advances within the area of computational intelligence. They consist of evolutionary algorithms applied to the design of fuzzy systems. Thanks to this hybridization, superb abilities are provided to fuzzy modeling in many different data science scenarios. This contribution is intended to comprise a position paper developing a comprehensive analysis of the evolutionary fuzzy systems research field. To this end, the 4 W questions are posed and addressed with the aim of understanding the current context of this topic and its significance. Specifically, it will be pointed out why evolutionary fuzzy systems are important from an explainable point of view, when they began, what they are used for, and where the attention of researchers should be directed to in the near future in this area. They must play an important role for the emerging area of eXplainable Artificial Intelligence (XAI) learning from data.

Simulating Transport Networks With a <italic>Physarum</italic> Foraging Model

Designing effective transport networks can be considered as one of the most debated problems in the area of computational intelligence. Some nature-inspired algorithms have shown excellent abilities in the adaptive network construction. In this aspect, a unique creature, called Physarum , has exhibited the computing capacity to optimize protoplasmic networks connecting distributed food sources. This inspires our work to design a Physarum foraging platform for constructing transport networks. Specifically, the traditional Physarum foraging model is adapted to construct transport networks in China. In order to get close to the real scenario, practical data are collected to build the environment of the Physarum foraging model and the structure of real transport networks. Some measurements in the domain of complex networks, such as average path length, network efficiency, topology robustness, and functional robustness, are used for performance comparison. The experimental results demonstrate that Physarum foraging models excel in constructing highly efficient and robust networks, which can be utilized for directing the design of transport networks in the real world.

A cloud model based DNA genetic algorithm for numerical optimization problems

Bio-inspired algorithms for optimization are significant topics in the areas of computational intelligence. Traditional genetic algorithm easily gets stuck at a local optimum, and often has slow convergent speed. To overcome these drawbacks, the Cloud model based genetic algorithm with DNA encoding (CM-DNAGA) is originally proposed in this study. The CM-DNAGA algorithm is based on not only the properties of randomness and stable tendency of the normal cloud model, but also the idea of GA with the bio-inspired coding method, i.e., DNA. In CM-DNAGA, a Y conditional normal cloud generator is used as the genetic crossover operator, and a basic normal cloud generator is used as the mutation operator. The simulation experiments are conducted on 12 numerical optimization functions, which evaluate the performance of the proposed algorithm. The experimental results indicate that the proposed method is a competitive optimizer in comparison with the three state-of-the-art heuristic algorithms, i.e. standard GA, PSO and RNA-GA.

CIS Publication Spotlight [Publication Spotlight

Presents a synopsis of the latest books in the area of computational intelligence.

Detecting and predicting the topic change of Knowledge-based Systems: A topic-based bibliometric analysis from 1991 to 2016

The journal Knowledge-based Systems (KnoSys) has been published for over 25 years, during which time its main foci have been extended to a broad range of studies in computer science and artificial intelligence. Answering the questions: “What is the KnoSys community interested in?” and “How does such interest change over time?” are important to both the editorial board and audience of KnoSys. This paper conducts a topic-based bibliometric study to detect and predict the topic changes of KnoSys from 1991 to 2016. A Latent Dirichlet Allocation model is used to profile the hotspots of KnoSys and predict possible future trends from a probabilistic perspective. A model of scientific evolutionary pathways applies a learning-based process to detect the topic changes of KnoSys in sequential time slices. Six main research areas of KnoSys are identified, i.e., expert systems, machine learning, data mining, decision making, optimization, and fuzzy, and the results also indicate that the interest of KnoSys communities in the area of computational intelligence is raised, and the ability to construct practical systems through knowledge use and accurate prediction models is highly emphasized. Such empirical insights can be used as a guide for KnoSys submissions.

CIS Publication Spotlight [Publication Spotlight

Presents a synopsis of the latest books in the area of computational intelligence.

Hibridan diagnostički sustav za male turbomlazne strojeve

A diagnostic system is one of the key components securing the safety of operation of a turbojet engine. There are many different engineering approaches how to design engine diagnostic systems applied in real-world conditions. We propose a novel approach for a diagnostic system design to be applied on the object of a small turbojet engine using thermal diagnostics combined with a more traditional model based approach. The combination of these two approaches together with application of methods from the area of computational intelligence create a hybrid diagnostic system, which can be integrated with a control system of the engine. The article also deals with experimental validation of the designed diagnostic system on the object of a small turbojet engine iSTC-21v in laboratory conditions.

CIS Publication Spotlight [Publication Spotlight

Presents a synopsis of the latest books in the area of computational intelligence.

Adaptive incremental learning of image semantics with application to social robot

Intelligent human–robot interaction is an interesting and challenging research topic both in areas of computational intelligence and robot control. In this paper we propose a novel adaptive and incremental image semantics learning framework based on the specific application platform of social robot. This endows the robot with the ability to learn to recognize new images based on previous human–robot interactions. In contrast with most of the intelligent image semantics learning works, which typically focus on how to recognize large scale of training data, this paper deals with how to learn image semantics from zero beginning and enrich the knowledge incrementally with human–robot interactions. In our framework, the user first presents unlabeled images to a humanoid robot for recognition; then the robot answers the user what׳s the image based on the semi-supervised incremental learning framework; thirdly user “teaches” the robot the right label of the image if the robot gives a wrong answer. Users can present more unlabeled images to the robot in the framework for teaching and learning. Extensive experiments and comparisons have validated the proposed methods with encouraging results. Our framework has a broad range of applications including education and rehabilitation.

A Novel Prediction Method for Early Recognition of Global Human Behaviour in Image Sequences

Human behaviour recognition has been, and still remains, a challenging problem that involves different areas of computational intelligence. The automated understanding of people activities from video sequences is an open research topic in which the computer vision and pattern recognition areas have made big efforts. In this paper, the problem is studied from a prediction point of view. We propose a novel method able to early detect behaviour using a small portion of the input, in addition to the capabilities of it to predict behaviour from new inputs. Specifically, we propose a predictive method based on a simple representation of trajectories of a person in the scene which allows a high level understanding of the global human behaviour. The representation of the trajectory is used as a descriptor of the activity of the individual. The descriptors are used as a cue of a classification stage for pattern recognition purposes. Classifiers are trained using the trajectory representation of the complete sequence. However, partial sequences are processed to evaluate the early prediction capabilities having a specific observation time of the scene. The experiments have been carried out using the three different dataset of the CAVIAR database taken into account the behaviour of an individual. Additionally, different classic classifiers have been used for experimentation in order to evaluate the robustness of the proposal. Results confirm the high accuracy of the proposal on the early recognition of people behaviours.

CIS Publication Spotlight [Publication Spotlight

Lists recently published books in the area of computational intelligence.

Prediction of shear capacity of channel shear connectors using the ANFIS model

Due to recent advancements in the area of Artificial Intelligence (AI) and computational intelligence, the application of these technologies in the construction industry and structural analysis has been made feasible. With the use of the Adaptive-Network-based Fuzzy Inference System (ANFIS) as a modelling tool, this study aims at predicting the shear strength of channel shear connectors in steel concrete composite beam. A total of 1200 experimental data was collected, with the input data being achieved based on the results of the push-out test and the output data being the corresponding shear strength which were recorded at all loading stages. The results derived from the use of ANFIS and the classical linear regressions (LR) were then compared. The outcome shows that the use of ANFIS produces highly accurate, precise and satisfactory results as opposed to the LR.

Single Document Text Summarization Using Clustering Approach Implementing for News Article

Text summarization is an old challenge in text mining but in dire need of researcher's attention in the areas of computational intelligence, machine learning and natural language processing. We extract a set of features from each sentence that helps identify its importance in the document. Every time reading full text is time consuming. Clustering approach is useful to decide which type of data present in document. In this paper we introduce the concept of k-mean clustering for natural language processing of text for word matching and in order to extract meaningful information from large set of offline documents, data mining document clustering algorithm are adopted. Traditional documents clustering algorithms use the full- text in the documents to generate feature vectors. Such methods often produce unsatisfactory results because there is much noisy information in documents. The varying-length problem of the documents is also a significant negative factor affecting the performance. This technique retrieves important sentence emphasize on high information richness in the sentence as well as high information retrieval. These multiple factors help to maximize coverage of each sentence by taking into account the sentence relatedness to all other document sentence. These related maximum sentence generated scores are clustered to generate the summary of the document. Thus we use k-mean clustering to these maximum sentences of the document and find the relation to extract clusters with most relevant sets in the document, these helps to find the summary of the document. The main purpose of k-mean clustering algorithm is to generate pre define length of summary having maximum informative sentences. In this paper we present the approach for automatic text summarization by extraction of sentences from the Reuters-21578 corpus which include newspaper articles and used clustering approach for extraction summary. Work done for Text Summarization is given in the section (II). Section (III) provided our methodology for Text Summarization, Section (IV) provide the result of our text summarization system.