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11th International Conference on Data Mining and Warehousing (ICDMW)

作者： Mishra, Vikash Singh, Vikram Natl Inst Technol Kurukshetra 136119 Haryana India

Modern day's queries are posed on database spread across the globe, this may impose a challenge on processing queries efficiently, and a strategy is required to generate optimal query plans. In distributed relational database systems, due to partitioning or replication on relations at multiple sites, the relations required by a query to answer, may be stored at multiple sites. This leads to an exponential increase in the number of possible equivalent alternatives or query plans for a user query. Though it is not computationally reasonable to explore exhaustively all possible query plans in a large search space, the query plan with most cost-effective option for query processing is measured necessary and must be generated for a given query. In this paper, an attempt has been made to generate such optimal query plans using parameter less optimization technique Teaching-learner based optimization (TLBO). The TLBO algorithm was observed to go one better than the other optimization algorithms for the multi-objective unconstrained and constrained benchmark problems. Experimental comparisons of this algorithm with the multi-objective GA based distributed query plan generation algorithm shows that for higher number of relations, the TLBO based algorithm is able to generate comparatively better quality Top-K query plans. (C) 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Aggregation based genetic algorithm Distributed query processing Top-K teacher learner based optimization VEGA

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Generating Optimal Query Plans for Distributed Query Processing using teacher-learner based optimization

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Procedia Computer Science 2015年 54卷 281-290页

作者： Vikash Mishra Vikram Singh National Institute of Technology Kurukshetra Haryana 136 119 India

关键词： Aggregation based genetic algorithm Distributed query processing Top-K teacher learner based optimization VEGA.

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Path optimization for multiple humanoid robot using TLBO based ANFIS controller in obscure environment

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Materials Today: Proceedings 2021年 47卷 2677-2684页

作者： Abhishek Kumar Kashyap Anish Pandey Dayal R. Parhi Abhishek Sharma Mechanical Engineering Department National Institute of Technology Rourkela 769008 Odisha India School of Mechanical Engineering Kalinga Institute of Industrial Technology Bhubaneswar 751024 Odisha India Department of Mechanical Engineering Manipal University Jaipur Jaipur 303007 Rajasthan India

Humanoid robot NAO is an intelligent reprogrammable agent designed to execute desired work using wireless control system by sensing the working environment at the same time. This article concentrates on the analysis of hybrid intelligent navigation approaches for single and multiple humanoid NAOs. It describes the optimization of a collision-free path in a static and dynamic terrain. The hybridization introduced is designed in two steps. ANFIS (Adaptive Network-based Fuzzy Inference System) controller produces a transitional driving angle (TDA) using a robot location with respect to obstacle distance in all directions. It is fed to teacher learner based optimization (TLBO) approach that produces optimum driving angle (ODA) for the humanoid robot to guide along with its predefined target. It optimizes the path selection by taking Euclidean distance and ODA as the key factor. The hybridized controller is examined in an environment that consists of single and multiple NAOs. Case of inter-collision may occur in the path planning of multiple humanoid NAOs. It is eliminated with the integration of the dining philosopher controller in the base algorithm to prioritized one robot towards the assigned target. Simulation results demonstrate the proposed controller's efficacy and explain that it gives optimized path length and travel time. In addition, a comparative study with previously applied effective algorithms is done.

关键词： teacher learner based optimization Adaptive network-based fuzzy inference system Multiple humanoid robots Inter-collision avoidance Dining philosopher controller

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Feature Selection Using Improved Teaching Learning based Algorithm on Chronic Kidney Disease Dataset

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Procedia Computer Science 2020年 171卷 1660-1669页

作者： Manonmani. M Sarojini Balakrishnan Research Scholar Department of Computer Science Avinashilingam Institute for Home Science and Higher Education for Women Coimbatore India Assistant Professor (SS) Department of Computer Science Avinashilingam Institute for Home Science and Higher Education for Women Coimbatore India

Feature selection plays an important role in almost any data mining application especially in medical data mining to solve the problem of ‘curse of dimensionality’ and provide early diagnosis with relevant features and high accuracy. Innumerable feature selection methods have been presented in state-of-arts literature to tackle the problems of high dimensional data. Many evolutionary and swarm intelligence algorithms find solutions based on algorithm-specific control parameters. However, it is a challenging task to identify the optimal feature subset using a feature selection algorithm that is not dependent on the controlling parameters of an algorithm that is specific to a particular problem in hand. Hence, the present research work is based on the working principle of the original TLBO algorithm which does not require any algorithm-specific parameters. The proposed research work is known as Improved teacher learner based optimization (ITLBO) algorithm which aims to select the best feature subset based on Chebyshev distance formula in the evaluation of the fitness function and common control parameters viz., population size and number of generations to find the optimal feature subset for early diagnosis of chronic diseases. The proposed feature selection technique was applied to Chronic Kidney Disease (CKD) dataset and has achieved a significant feature reduction of 36% compared to the feature reduction of 25 % obtained by applying the original TLBO algorithm. The derived optimal feature subset obtained from TLBO algorithm and feature subset obtained from ITLBO algorithm is validated by evaluating the accuracy of Support Vector Machine (SVM), Convolution Neural Networks (CNN) and Gradient Boosting classification algorithms. Experimental results reveal that there is an overall improvement of classification accuracy for the three algorithms for the derived feature subset from the proposed feature selection algorithm compared to the original TLBO algorithm.

关键词： teacher learner based optimization Improved teacher learner based optimization Chronic Kidney Disease Support Vector Machine Convolution Neural Networks Gradient Boosting optimization techniques

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