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International Journal of Intelligent Systems Design and Computing
Number of Followers: 0  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 2052-8477 - ISSN (Online) 2052-8485
Published by Inderscience Publishers Homepage  [435 journals]
  • Optimal control of cyber physical vehicle systems
    • Authors: Kaustav Jyoti Borah, Jutika Borah, Mohan Kantipudi
      Pages: 205 - 213
      Abstract: Cyber-physical vehicle systems (CPVSs) are advancing due to progress in real-time applications, control and artificial intelligence. Multi-objective design optimisation maximises CPS efficiency, capability and safety, while online regulation enables the vehicle to be responsive to disturbances, modelling errors and uncertainties. CPVS optimisation occurs at design-time and at run-time. We will survey the run-time cooperative optimisation or co-optimisation of cyber and physical systems, which have historically been considered separately. A run-time CPVS is also cooperatively regulated or co-regulated when cyber and physical resources are utilised in a manner that is responsive to both cyber and physical system requirements. This paper surveys research that considers both cyber and physical resources in co-optimisation and co-regulation schemes with applications to mobile robotic and vehicle systems. Time-varying sampling patterns, sensor scheduling, anytime control, feedback scheduling, task and motion planning and resource sharing are examined. Cyber-physical systems will transform how we interact with the physical world around us. Many grand challenges wait in the economically vital domains of transportation, healthcare, manufacturing, agriculture, energy, defence, aerospace and buildings.
      Keywords: new frontiers; control; real-time control; optimisation; optimal control; robotics
      Citation: International Journal of Intelligent Systems Design and Computing, Vol. 1, No. 3/4 (2017) pp. 205 - 213
      PubDate: 2018-03-30T23:20:50-05:00
      DOI: 10.1504/IJISDC.2017.090860
      Issue No: Vol. 1, No. 3/4 (2018)
       
  • A prediction-based handoff scheme for QoS in WLAN systems
    • Authors: Sanjay Kumar Biswash, Pavan Kumar Mishra
      Pages: 214 - 230
      Abstract: In this paper, we introduce a technique to reduce the authentication overhead for handoff process in wireless local area networks (WLAN). It is based on user mobility prediction to reduce the network scanning area during the handoff procedure. The central server facilitates an intelligent mobility prediction mechanism to identify the next possible mobile access points for mobile subscriber. The predicted information is managed by an event log table, and it helps to reduce the authorisation overheads for cellular networks. It follows the reserve channel scheme to maintain the QoS for users during the call and handoff procedure and the handover-call has higher priority over local initiated call. The analytical model shows the effectiveness and efficiency of proposed work, we apply the Markov random walk model and M/M/1 queuing model to formulate our work. It is compared with the tradition technique, and has 40% better result than legacy techniques.
      Keywords: wireless access point; handoff management; pre-authentication; mobility prediction; network overheads
      Citation: International Journal of Intelligent Systems Design and Computing, Vol. 1, No. 3/4 (2017) pp. 214 - 230
      PubDate: 2018-03-30T23:20:50-05:00
      DOI: 10.1504/IJISDC.2017.090872
      Issue No: Vol. 1, No. 3/4 (2018)
       
  • A survey of detecting pedestrians from low resolution imagery
    • Authors: Kyaw Kyaw Htike, Neoh Siew Chin, Zaw Zaw Htike, Choo Wou Onn
      Pages: 231 - 261
      Abstract: Being able to detect pedestrians in image or video has numerous potential benefits in many diverse applications such as image retrieval, elderly monitoring and safety, person counting and driver assistance systems. Although much work have been done for pedestrian detection, recent state-of-the-art research indicate that a lot of improvements still need to be made, especially when it comes to low resolution imagery. Despite a number of review papers on pedestrian detection that have been published, there is a great need for a survey paper that focuses on pedestrian detection for low resolution data. In this paper, we perform an in-depth critical analysis and review of the most representative and relevant papers in this area, including identification and breaking down of the pipeline for low resolution pedestrian detection systems, as well as, discussing and analysing the underlying causes behind low resolution data as well as recommending potential solutions.
      Keywords: low resolution; detecting pedestrians; object detection; image analytics; computer vision
      Citation: International Journal of Intelligent Systems Design and Computing, Vol. 1, No. 3/4 (2017) pp. 231 - 261
      PubDate: 2018-03-30T23:20:50-05:00
      DOI: 10.1504/IJISDC.2017.090877
      Issue No: Vol. 1, No. 3/4 (2018)
       
  • Stochastic modelling for traffic flow: a review
    • Authors: Kaustav Jyoti Borah, Abul Abbas Barbhuiya
      Pages: 262 - 271
      Abstract: The stochastic process and estimation from the review paper for the breakdown flow contains two random flow breakdowns variables. Breakdown flow happens when the two random variables, breakdown speed and breakdown duration, occurs at the homogeneous level. The model follows microscopic model which are homogeneous and continues in a heterogeneous level for a macroscopic model. The models are based from the vehicle changes at different levels of their speed. The mathematical process used in order to obtain the random variables are Weibull probability distribution for the breakdown speed and also the probability density function for the breakdown duration as an estimation also known as the hazard function. Simulation using Monte Carlo method will compute the random sampling from the breakdown variables and breakdown duration into a macroscopic model. The results will compare the actual data collected from Caltrans in 2007 and the mathematical model and simulation will be the same traffic flow behaviour and characteristics.
      Keywords: breakdown duration; breakdown speed; breakdown flow; hazard function; macroscopic model; microscopic model; Monte Carlo simulation; Weidull probability distribution
      Citation: International Journal of Intelligent Systems Design and Computing, Vol. 1, No. 3/4 (2017) pp. 262 - 271
      PubDate: 2018-03-30T23:20:50-05:00
      DOI: 10.1504/IJISDC.2017.090873
      Issue No: Vol. 1, No. 3/4 (2018)
       
  • K-means Atkinson clustering approach for collaborative
           filtering-based recommendation system
    • Authors: Surya Kant, Tripti Mahara
      Pages: 272 - 285
      Abstract: The amount of information generated by web is growing rapidly every day and results in information overload. The information overload problem makes recommendation system necessary. Collaborative filtering is one of the most successful approaches to design a recommendation system. The key idea of this technique is based on the common interest of users. If the user has similar taste in past for a set of items, then they will share common taste in future. However, sparsity and scalability are major drawbacks of this prosperous approach affecting the quality of recommendations. In this paper, a clustering-based recommendation algorithm is proposed. The clustering technique has been used to form neighbourhoods (groups of users who have similar preferences) of active user. It exploits underlying data correlation structures to choose the initial centroid for k-means. The experimental results on three benchmark datasets, MovieLens 100k, MovieLens 1M and Jester, demonstrates that proposed method exhibits superior accuracy in comparison to the traditional k-means based recommender systems.
      Keywords: recommendation system; collaborative filtering; clustering; information filtering
      Citation: International Journal of Intelligent Systems Design and Computing, Vol. 1, No. 3/4 (2017) pp. 272 - 285
      PubDate: 2018-03-30T23:20:50-05:00
      DOI: 10.1504/IJISDC.2017.090866
      Issue No: Vol. 1, No. 3/4 (2018)
       
  • An improved harmony search-based functional link higher order ANN for
           nonlinear data classification
    • Authors: Bighnaraj Naik, Janmenjoy Nayak, Himansu Sekhar Behera, Ajith Abraham
      Pages: 286 - 318
      Abstract: To obtain the optimal set of weights in any higher order artificial neural network, it is often laborious to adjust the set of weights by using appropriate learning algorithm. In this paper, an improved variant of harmony search (HS), called improved harmony search (IHS) along with gradient descent learning (GDL) is used with functional link artificial neural network (FLANN) for the task of classification in data mining. IHS performs better than HS by eliminating constant parameters [bandwidth (bw), pitch adjustment rate (PAR)] in HS algorithm and incorporating changes dynamically in PAR and bw with iteration. The searching capability of IHS to obtain optimal harmony is used along with GDL to discover optimal set of weights for FLANN model. The proposed IHS-GDL-FLANN is implemented in MATLAB and compared with other alternatives. In order to get statistical correctness of results, the proposed method is analysed by using various statistical analysis under null-hypothesis.
      Keywords: improved harmony search; IHS; gradient descent learning; GDL; functional link artificial neural network; FLANN; data mining; classification; machine learning
      Citation: International Journal of Intelligent Systems Design and Computing, Vol. 1, No. 3/4 (2017) pp. 286 - 318
      PubDate: 2018-03-30T23:20:50-05:00
      DOI: 10.1504/IJISDC.2017.090856
      Issue No: Vol. 1, No. 3/4 (2018)
       
  • Periodic pattern mining in weighted dynamic networks
    • Authors: Anand Gupta, Hardeo Kumar Thakur, Anshul Garg
      Pages: 319 - 338
      Abstract: Graph is one of the media to represent and summarise interactions in a time varying network. Often, interactions repeat after a fixed interval of time and exhibit temporal periodicity. Existing algorithms focus either on the structure or on the weight of periodic interactions individually. But, for instance, a stock analyst requires evidence of both structure and weight (here, price) of the stock pairs to make prediction and discovers information about the profit producing stocks and the actual profit. On performing experiments using existing algorithms explicitly, it is observed that the efficiency is lost in such applications. Hence, in this paper, we provide an efficient framework based on available algorithms to mine periodic patterns both on structure and weight in a weighted dynamic network. The proposed framework consists of a mapping between interactions that are periodic on structure and weight. We have performed experiments on synthetic and real world datasets. The results validate the scalability and practical feasibility of the proposed framework.
      Keywords: dynamic graph; periodic graph; frequent graph; weighted graph
      Citation: International Journal of Intelligent Systems Design and Computing, Vol. 1, No. 3/4 (2017) pp. 319 - 338
      PubDate: 2018-03-30T23:20:50-05:00
      DOI: 10.1504/IJISDC.2017.090857
      Issue No: Vol. 1, No. 3/4 (2018)
       
 
 
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