Journals / IASC / Vol.24, No.4

  • ARTICLE

    Hybrid Architecture for Autonomous Load Balancing in Distributed Systems Based on Smooth Fuzzy Function

    Moazam Ali, Susmit Bagchi*
    Intelligent Automation & Soft Computing, Vol.24, No.4, 2018, DOI:10.31209/2018.100000043
    Abstract Due to the rapid advancements and developments in wide area networks and powerful computational resources, the load balancing mechanisms in distributed systems have gained pervasive applications covering wired as well as mobile distributed systems. In large-scale distributed systems, sharing of distributed resources is required for enhancing overall resource utilization. This paper presents a comprehensive study and detailed comparative analysis of different load balancing algorithms employing fuzzy logic and mobile agents. We have proposed a hybrid architecture for integrated load balancing and monitoring in distributed computing systems employing fuzzy logic and autonomous mobile agents. Furthermore, we have proposed a smooth and… More >

  • ARTICLE

    On the Use of Genetic Algorithm for Solving Re-entrant Flowshop Scheduling with Sum-of-processing-times-based Learning Effect to Minimize Total Tardiness

    Win-Chin Lina, Chin-Chia Wua, Kejian Yub, Yong-Han Zhuanga, Shang-Chia Liuc
    Intelligent Automation & Soft Computing, Vol.24, No.4, pp. 671-681, 2018, DOI:10.1080/10798587.2017.1302711
    Abstract Most research studies on scheduling problems assume that a job visits certain machines only one time. However, this assumption is invalid in some real-life situations. For example, a job may be processed by the same machine more than once in semiconductor wafer manufacturing or in a printed circuit board manufacturing machine. Such a setting is known as the “re-entrant flowshop”. On the other hand, the importance of learning effect present in many practical situations such as machine shop, in different branches of industry and for a variety of corporate activities, in shortening life cycles, and in an increasing diversity of… More >

  • ARTICLE

    Automatic FIBEX Generation for Migration from CAN Message Description Format to Flexray Fibex Format

    Young Hun Songa, Suk Leea, Kyoung Nam Hab, Kyung Chang Leec
    Intelligent Automation & Soft Computing, Vol.24, No.4, pp. 683-691, 2018, DOI:10.1080/10798587.2017.1302712
    Abstract Recently, FlexRay was developed to replace the controller area network (CAN) protocol in the chassis network systems to provide high-speed data transmission as well as hardware redundancy for safety. However, FlexRay network design is more complicated than with CAN protocol, which has been an in-vehicle network (IVN) standard for car manufacturers for decades, because the FlexRay has many parameters such as the base cycle or slot lengths. To simplify the FlexRay network design and assist vehicle network designers in configuring a FlexRay network, this paper presents an automatic field bus exchange format (FIBEX) generation method for migration from the CAN… More >

  • ARTICLE

    A Fuzzy Multi-Criteria Decision Analysis Approach for the Evaluation of the Network Service Providers in Turkey

    Serkan Ballıa, Mustafa Tukerb
    Intelligent Automation & Soft Computing, Vol.24, No.4, pp. 693-699, 2018, DOI:10.1080/10798587.2017.1306968
    Abstract Heterogeneous networks are environments where networks having different topologies and technologies can be connected. In an environment including more than one heterogeneous access network, selection of a bad network may lead to emergence of negative results such as high cost and poor service experience for the users. Ensuring the use of the most effective access network for the personal needs of individuals is a complex decision-making process. In the present study, a multicriteria decision-making system employing fuzzy logic was developed to evaluate and select network service providers in Turkey. Fuzzy logic was used for the criteria containing uncertain and unclear… More >

  • ARTICLE

    An Intelligent Incremental Filtering Feature Selection and Clustering Algorithm for Effective Classification

    U. Kanimozhi, D. Manjula
    Intelligent Automation & Soft Computing, Vol.24, No.4, pp. 701-709, 2018, DOI:10.1080/10798587.2017.1307626
    Abstract We are witnessing the era of big data computing where computing the resources is becoming the main bottleneck to deal with those large datasets. In the case of high-dimensional data where each view of data is of high dimensionality, feature selection is necessary for further improving the clustering and classification results. In this paper, we propose a new feature selection method, Incremental Filtering Feature Selection (IF2S) algorithm, and a new clustering algorithm, Temporal Interval based Fuzzy Minimal Clustering (TIFMC) algorithm that employs the Fuzzy Rough Set for selecting optimal subset of features and for effective grouping of large volumes of… More >

  • ARTICLE

    A Multi Criterion Fuzzy Based Energy Efficient Routing Protocol for Ad hoc Networks

    Geetha N., Sankar A.
    Intelligent Automation & Soft Computing, Vol.24, No.4, pp. 711-719, 2018, DOI:10.1080/10798587.2017.1309003
    Abstract The routing protocol for an ad hoc network should be efficient in utilizing the available resources to prolong the network lifetime. A Multi Criterion Fuzzy based Energy Efficient Routing Protocol (MCFEER) for Ad hoc Networks selects the path on constraints like bandwidth, battery life, hop count and buffer occupancy. In the route discovery phase, fuzzy system is applied for optimal route selection by destination node leading to successful data transmission. Multiple stable paths are preserved in route cache for usage during the route maintenance phase. The results are competitive when compared with Power aware Energy Efficient Routing (PEER) protocol using… More >

  • ARTICLE

    A Novel Strategy for Mining Highly Imbalanced Data in Credit Card Transactions

    Masoumeh Zareapoor, Jie Yang
    Intelligent Automation & Soft Computing, Vol.24, No.4, pp. 721-727, 2018, DOI:10.1080/10798587.2017.1321228
    Abstract The design of an efficient credit card fraud detection technique is, however, particularly challenging, due to the most striking characteristics which are; imbalancedness and non-stationary environment of the data. These issues in credit card datasets limit the machine learning algorithm to show a good performance in detecting the frauds. The research in the area of credit card fraud detection focused on detection the fraudulent transaction by analysis of normality and abnormality concepts. Balancing strategy which is designed in this paper can facilitate classification and retrieval problems in this domain. In this paper, we consider the classification problem in supervised learning… More >

  • ARTICLE

    Portrait Vision Fusion for Augmented Reality

    Li-Hong Juanga, Ming-Ni Wub, Feng-Mao Tsoub
    Intelligent Automation & Soft Computing, Vol.24, No.4, pp. 739-745, 2018, DOI:10.1080/10798587.2017.1327549
    Abstract Kinect(+openCV); Dynamic portrait segmentation; Skeletal tracking; Edge transparent processing; Video interactive More >

  • ARTICLE

    Analysis of Collaborative Brain Computer Interface (BCI) Based Personalized GUI for Differently Abled

    M. Umaa,c, T. Sheelab
    Intelligent Automation & Soft Computing, Vol.24, No.4, pp. 747-757, 2018, DOI:10.1080/10798587.2017.1332804
    Abstract Brain-Computer Interfaces (BCI) use Electroencephalography (EEG) signals recorded from the brain scalp, which enable a communication between the human and the outside world. The present study helps the patients who are people locked-in to manage their needs such as accessing of web url’s, sending/receiving sms to/from mobile device, personalized music player, personalized movie player, wheelchair control and home appliances control. In the proposed system, the user needs are designed as a button in the form of a matrix, in which the main panel of rows and columns button is flashed in 3 sec intervals. Subjects were asked to choose the… More >

  • ARTICLE

    Performance Analyses of Nature-inspired Algorithms on the Traveling Salesman’s Problems for Strategic Management

    Julius Beneoluchi Odilia, Mohd Nizam Mohmad Kahara, A. Noraziaha,b, M. Zarinac, Riaz Ul Haqa
    Intelligent Automation & Soft Computing, Vol.24, No.4, pp. 759-769, 2018, DOI:10.1080/10798587.2017.1334370
    Abstract This paper carries out a performance analysis of major Nature-inspired Algorithms in solving the benchmark symmetric and asymmetric Traveling Salesman’s Problems (TSP). Knowledge of the workings of the TSP is very useful in strategic management as it provides useful guidance to planners. After critical assessments of the performances of eleven algorithms consisting of two heuristics (Randomized Insertion Algorithm and the Honey Bee Mating Optimization for the Travelling Salesman’s Problem), two trajectory algorithms (Simulated Annealing and Evolutionary Simulated Annealing) and seven population-based optimization algorithms (Genetic Algorithm, Artificial Bee Colony, African Buffalo Optimization, Bat Algorithm, Particle Swarm Optimization, Ant Colony Optimization and… More >

  • ARTICLE

    Output Consensus of Heterogeneous Multi-agent Systems under Directed Topologies via Dynamic Feedback

    Xiaofeng Liu, Siqi An, Dongxu Zhang
    Intelligent Automation & Soft Computing, Vol.24, No.4, pp. 771-775, 2018, DOI:10.1080/10798587.2017.1337667
    Abstract This paper discusses the problem of dynamic output consensus for heterogeneous multi-agent systems (MAS) with fixed topologies. All the agents possess unique linear dynamics, and only the output information of each agent is delivered throughout the communication digraphs. A series of conditions and protocols are set for reaching the consensus. With the proper feedback controllers, the output consensus of the overall system is guaranteed. An application illustrates the theorems. More >

  • ARTICLE

    Improving Performance Prediction on Education Data with Noise and Class Imbalance

    Akram M. Radwana,b, Zehra Cataltepea,c
    Intelligent Automation & Soft Computing, Vol.24, No.4, pp. 777-783, 2018, DOI:10.1080/10798587.2017.1337673
    Abstract This paper proposes to apply machine learning techniques to predict students’ performance on two real-world educational data-sets. The first data-set is used to predict the response of students with autism while they learn a specific task, whereas the second one is used to predict students’ failure at a secondary school. The two data-sets suffer from two major problems that can negatively impact the ability of classification models to predict the correct label; class imbalance and class noise. A series of experiments have been carried out to improve the quality of training data, and hence improve prediction results. In this paper,… More >

  • ARTICLE

    Feature Selection for Activity Recognition from Smartphone Accelerometer Data

    Juan C. Quiroza, Amit Banerjeeb, Sergiu M. Dascaluc, Sian Lun Laua
    Intelligent Automation & Soft Computing, Vol.24, No.4, pp. 785-793, 2018, DOI:10.1080/10798587.2017.1342400
    Abstract We use the public Human Activity Recognition Using Smartphones (HARUS) data-set to investigate and identify the most informative features for determining the physical activity performed by a user based on smartphone accelerometer and gyroscope data. The HARUS data-set includes 561 time domain and frequency domain features extracted from sensor readings collected from a smartphone carried by 30 users while performing specific activities. We compare the performance of a decision tree, support vector machines, Naive Bayes, multilayer perceptron, and bagging. We report the various classification performances of these algorithms for subject independent cases. Our results show that bagging and the multilayer… More >

  • ARTICLE

    Quad-Rotor Directional Steering System Controller Design Using Gravitational Search Optimization

    M. A. Kamela, M. A. Abidob, Moustafa Elshafeic
    Intelligent Automation & Soft Computing, Vol.24, No.4, pp. 795-805, 2018, DOI:10.1080/10798587.2017.1342414
    Abstract Directional Steering System (DSS) has been established for well drilling in the oilfield in order to accomplish high reservoir productivity and to improve accessibility of oil reservoirs in complex locations. In this paper, a novel feedback linearization controller to cancel the nonlinear dynamics of a DSS is proposed. The proposed controller design problem is formulated as an optimization problem for optimal settings of the controller feedback gains. Gravitational Search Algorithm (GSA) is developed to search for optimal settings of the proposed controller. The objective function considered is to minimize the tracking error and drilling efforts. In this study, the DSS… More >

  • ARTICLE

    A Novel Cardholder Behavior Model for Detecting Credit Card Fraud

    Yiğit Kültür, Mehmet Ufuk Çağlayan
    Intelligent Automation & Soft Computing, Vol.24, No.4, pp. 807-817, 2018, DOI:10.1080/10798587.2017.1342415
    Abstract Because credit card fraud costs the banking sector billions of dollars every year, decreasing the losses incurred from credit card fraud is an important driver for the sector and end-users. In this paper, we focus on analyzing cardholder spending behavior and propose a novel cardholder behavior model for detecting credit card fraud. The model is called the Cardholder Behavior Model (CBM). Two focus points are proposed and evaluated for CBMs. The first focus point is building the behavior model using single-card transactions versus multi-card transactions. As the second focus point, we introduce holiday seasons as spending periods that are different… More >

  • ARTICLE

    Friends Classification of Ego Network Based on Combined Features

    Jing Jiaa, Tinghuai Mab, Fan Xinga, William Faraha, Donghai Guana,c
    Intelligent Automation & Soft Computing, Vol.24, No.4, pp. 819-827, 2018, DOI:10.1080/10798587.2017.1355656
    Abstract Ego networks consist of a user and his/her friends and depending on the number of friends a user has, makes them cumbersome to deal with. Social Networks allow users to manually categorize their “circle of friends”, but in today’s social networks due to the unlimited number of friends a user has, it is imperative to find a suitable method to automatically administrate these friends. Manually categorizing friends means that the user has to regularly check and update his circle of friends whenever the friends list grows. This may be time consuming for users and the results may not be accurate… More >

  • ARTICLE

    System Integration for Cognitive Model of a Robot Partner

    Jinseok Woo, János Botzheim, Naoyuki Kubota
    Intelligent Automation & Soft Computing, Vol.24, No.4, pp. 829-841, 2018, DOI:10.1080/10798587.2017.1364919
    Abstract This paper introduces the integrated system of a smart-device-based cognitive robot partner called iPhonoid-C. Interaction with a robot partner requires many elements, including verbal communication, nonverbal communication, and embodiment as well. A robot partner should be able to understand human sentences, as well as nonverbal information such as human gestures. In the proposed system, the robot has an emotional model connecting the input information from the human with the robot’s behavior. Since emotions are involved in human natural communication, and emotion has a significant impact on humans’ actions, it is important to develop an emotional model for the robot partner… More >

  • ARTICLE

    An Efficient Optimized Handover in Cognitive Radio Networks Using Cooperative Spectrum Sensing

    H. Anandakumara, K. Umamaheswarib
    Intelligent Automation & Soft Computing, Vol.24, No.4, pp. 843-849, 2018, DOI:10.1080/10798587.2017.1364931
    Abstract Cognitive radio systems necessitate the incorporation of cooperative spectrum sensing among cognitive users to increase the reliability of detection. We have found that cooperative spectrum sensing is not only advantageous, but is also essential to avoid interference with any primary users. Interference by licensed users becomes a chief concern and issue, which affects primary as well as secondary users leading to restrictions in spectrum sensing in cognitive radios. When the number of cognitive users increases, the overheads of the systems, which are meant to report the sensing results to the common receiver, which becomes massive. When the spectrum, which is… More >

  • ARTICLE

    An Efficient Adaptive Network-Based Fuzzy Inference System with Mosquito Host-Seeking For Facial Expression Recognition

    M. Carmel Sobia1, A. Abudhahir2
    Intelligent Automation & Soft Computing, Vol.24, No.4, pp. 869-881, 2018, DOI:10.31209/2018.100000014
    Abstract In this paper, an efficient facial expression recognition system using ANFIS-MHS (Adaptive Network-based Fuzzy Inference System with Mosquito Host-Seeking) has been proposed. The features were extracted using MLDA (Modified Linear Discriminant Analysis) and then the optimized parameters are computed by using mGSO (modified Glow-worm Swarm Optimization).The proposed system recognizes the facial expressions using ANFIS-MHS. The experimental results demonstrate that the proposed technique is performed better than existing classification schemes like HAKELM (Hybridization of Adaptive Kernel based Extreme Learning Machine), Support Vector Machine (SVM) and Principal Component Analysis (PCA). The proposed approach is implemented in MATLAB. More >

  • ARTICLE

    Hierarchical Optimization of Network Resource for Heterogeneous Service in Cloud Scenarios

    Dong Huanga,b, Yong Baib, Jingcheng Liuc, Hongtao Chend, Jinghua Lind, Jingjing Wud
    Intelligent Automation & Soft Computing, Vol.24, No.4, pp. 883-889, 2018, DOI:10.1080/10798587.2017.1327634
    Abstract With limited homogeneous and heterogeneous resources in a cloud computing system, it is not feasible to successively expand network infrastructure to adequately support the rapid growth in the cloud service. In this paper, an approach for optimal transmission of hierarchical network for heterogeneous service in Cloud Scenarios was presented. Initially, the theoretical optimal transmission model of a common network was transformed into the hierarchical network with the upper and lower optimization transmission model. Furthermore, the computation simplification and engineering transformation were presented for an approximation method at the low cost of computational complexity. In the final section, the average delay… More >

  • ARTICLE

    Statistical Analysis and Multimodal Classification on Noisy Eye Tracker and Application Log Data of Children with Autism and ADHD

    Mahiye Uluyagmur Ozturka, Ayse Rodopman Armanb, Gresa Carkaxhiu Bulutc, Onur Tugce Poyraz Findikb, Sultan Seval Yilmazd, Herdem Aslan Gencb, M. Yanki Yazgane,f, Umut Tekera, Zehra Cataltepea
    Intelligent Automation & Soft Computing, Vol.24, No.4, pp. 891-905, 2018, DOI:10.31209/2018.100000058
    Abstract Emotion recognition behavior and performance may vary between people with major neurodevelopmental disorders such as Autism Spectrum Disorder (ASD), Attention Deficit Hyperactivity Disorder (ADHD) and control groups. It is crucial to identify these differences for early diagnosis and individual treatment purposes. This study represents a methodology by using statistical data analysis and machine learning to provide help to psychiatrists and therapists on the diagnosis and individualized treatment of participants with ASD and ADHD. In this paper we propose an emotion recognition experiment environment and collect eye tracker fixation data together with the application log data (APL). In order to detect… More >

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