Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

Browse

Subject: search.filters.subject.Cat Swarm Optimization

Search Results

Now showing 1 - 4 of 4
  • No Thumbnail Available
    Item
    A hybrid bioinspired algorithm for facial emotion recognition using CSO-GA-PSO-SVM
    (Institute of Electrical and Electronics Engineers Inc., 2015) Vivek, T.V.; Guddeti, G.
    Human-Computer Interaction gets more natural when the machine can detect human emotions faster and accurate. A lot of research is being carried out in the field of affective computing in order to improve the accuracy with speed. Bio-inspired algorithms for feature extraction and classification stages, has improved accuracy and speed further. In this paper, we propose a hybrid algorithm using CSO (Cat Swarm Optimization) with PSO (Particle Swarm Optimization) and GA (Genetic Algorithm) for emotion recognition (ER). This bio inspired algorithm in conjunction with the support vector machine (SVM) will find an optimal feature set from a bigger set. Results from CK+ (Cohn Kanade) [1] dataset demonstrate that our proposed method using CSO-GA-PSOSVM outperforms Emotion Recognition System with CSOSVM by 10.5% in accuracy. This paper also proposes a new E-Learning [2] system to demonstrate its effectiveness and efficiency in real-time scenario. The proposed algorithm is applied over the facial characteristics captured from students in teaching-learning environment. The optimized feature vector obtained is passed to the SVM classifier for classification. Experimental results yield 99% classification accuracy in a person dependent mode with six basic emotions namely Happy, Sad, Anger, Disgust, Surprise and Neutral. © 2015 IEEE.
  • No Thumbnail Available
    Item
    A hybrid community detection based on evolutionary algorithms in social networks
    (Institute of Electrical and Electronics Engineers Inc., 2016) Jami, V.; Guddeti, G.R.
    In social network analysis, community detection is an optimization problem of finding out partitions of maximum modularity density from a network. It is a NP-hard problem which can be done using evolutionary algorithms such as Particle Swarm Optimization, Cat Swarm Optimization, Genetic Algorithm and Genetic Algorithm with Simulated Annealing. In this work, we proposed an algorithm based on Genetic Algorithm with Simulated annealing for not being trapped into local optimal solution which is giving more better results. The main motto of our work is to get better communities with low computation cost. We tested our proposed algorithm on three standard datasets such as Zachary's Karate Club Dataset, American College Football and Dolphin Social Network Dataset. Experimental results demonstrate that our proposed algorithm outperforms state of the art approaches. © 2016 IEEE.
  • No Thumbnail Available
    Item
    Fast Convergence to Near Optimal Solution for Job Shop Scheduling Using Cat Swarm Optimization
    (Springer Verlag service@springer.de, 2017) Dani, V.; Sarswat, A.; Swaroop, V.; Domanal, S.; Guddeti, G.R.M.
    Job Shop Scheduling problem has wide range of applications. However it being a NP-Hard optimization problem, always finding an optimal solution is not possible in polynomial amount of time. In this paper we propose a heuristic approach to find near optimal solution for Job Shop Scheduling Problem in predetermined amount of time using Cat Swarm Optimization. Novelty in our approach is our non-conventional way of representing position of cat in search space that ensures advantage of spatial locality is taken. Further while exploring the search space using randomization, we never explore an infeasible solution. This reduces search time. Our proposed approach outperforms some of the conventional algorithms and achieves nearly 86% accuracy, while restricting processing time to one second. © 2017, Springer International Publishing AG.
  • No Thumbnail Available
    Item
    On demand Virtual Machine allocation and migration at cloud data center using Hybrid of Cat Swarm Optimization and Genetic Algorithm
    (Institute of Electrical and Electronics Engineers Inc., 2017) Sharma, N.K.; Guddeti, G.R.M.
    This paper deals with the energy saving at the data center using energy aware Virtual Machines (VMs) allocation and migration. The multi-objective based VMs allocation using Hybrid Genetic Cat Swarm Optimization (HGACSO) algorithm saves the energy consumption as well as also reduces resource wastage. Further consolidating VMs onto the minimal number of Physical Machines (PMs) using energy efficient VMs migration, we can shut down idle PMs for enhancing the energy efficiency at a cloud data center. The experimental results show that our proposed HGACSO VM allocation and energy efficient VM migration techniques achieved the energy efficiency and minimization of resource wastage. © 2016 IEEE.