Anupam Shukla

Bio: Anupam Shukla is an academic researcher from Indian Institute of Information Technology and Management, Gwalior. The author has contributed to research in topic(s): Artificial neural network & Motion planning. The author has an hindex of 22, co-authored 215 publication(s) receiving 1896 citation(s). Previous affiliations of Anupam Shukla include Indian Institutes of Information Technology.

Fusion of probabilistic A* algorithm and fuzzy inference system for robotic path planning

Abstract: Robotic Path planning is one of the most studied problems in the field of robotics. The problem has been solved using numerous statistical, soft computing and other approaches. In this paper we solve the problem of robotic path planning using a combination of A* algorithm and Fuzzy Inference. The A* algorithm does the higher level planning by working on a lower detail map. The algorithm finds the shortest path at the same time generating the result in a finite time. The A* algorithm is used on a probability based map. The lower level planning is done by the Fuzzy Inference System (FIS). The FIS works on the detailed graph where the occurrence of obstacles is precisely known. The FIS generates smoother paths catering to the non-holonomic constraints. The results of A* algorithm serve as a guide for FIS planner. The FIS system was initially generated using heuristic rules. Once this model was ready, the fuzzy parameters were optimized using a Genetic Algorithm. Three sample problems were created and the quality of solutions generated by FIS was used as the fitness function of the GA. The GA tried to optimize the distance from the closest obstacle, total path length and the sharpest turn at any time in the journey of the robot. The resulting FIS was easily able to plan the path of the robot. We tested the algorithm on various complex and simple paths. All paths generated were optimal in terms of path length and smoothness. The robot was easily able to escape a variety of obstacles and reach the goal in an optimal manner.

Robotic path planning in static environment using hierarchical multi-neuron heuristic search and probability based fitness

Abstract: Path Planning is a classical problem in the field of robotics The problem is to find a path of the robot given the various obstacles The problem has attracted the attention of numerous researchers due to the associated complexities, uncertainties and real time nature In this paper we propose a new algorithm for solving the problem of path planning in a static environment The algorithm makes use of an algorithm developed earlier by the authors called Multi-Neuron Heuristic Search (MNHS) This algorithm is a modified A^@? algorithm that performs better than normal A^@? when heuristics are prone to sharp changes This algorithm has been implemented in a hierarchical manner, where each generation of the algorithm gives a more detailed path that has a higher reaching probability The map used for this purpose is based on a probabilistic approach where we measure the probability of collision with obstacle while traveling inside the cell As we decompose the cells, the cell size reduces and the probability starts to touch 0 or 1 depending upon the presence or absence of obstacles in the cell In this approach, it is not compulsory to run the entire algorithm We may rather break after a certain degree of certainty has been achieved We tested the algorithm in numerous situations with varying degrees of complexities The algorithm was able to give an optimal path in all the situations given The standard A^@? algorithm failed to give results within time in most of the situations presented

Egyptian Vulture Optimization Algorithm – A New Nature Inspired Meta-heuristics for Knapsack Problem

Abstract: In this paper we have introduced for the first time a new nature inspired meta-heuristics algorithm called Egyptian Vulture Optimization Algorithm which primarily favors combinatorial optimization problems. The algorithm is derived from the nature, behavior and key skills of the Egyptian Vultures for acquiring food for leading their livelihood. These spectacular, innovative and adaptive acts make Egyptian Vultures as one of the most intelligent of its kind among birds. The details of the bird’s habit and the mathematical modeling steps of the algorithm are illustrated demonstrating how the meta-heuristics can be applied for global solutions of the combinatorial optimization problems and has been studied on the traditional 0/1 Knapsack Problem (KSP) and tested for several datasets of different dimensions. The results of application of the algorithm on KSP datasets show that the algorithm works well w.r.t optimal value and provide the scope of utilization in similar kind of problems like path planning and other combinatorial optimization problems.

A survey of nature-inspired algorithms for feature selection to identify Parkinson's disease

Abstract: We perform a comparative analysis of nature inspired-algorithms for feature selection to aid the classification of affected Parkinson's patients from the rest.Feature selection was applied to datasets of gait and speech of Parkinson's patients.Binary Bat Algorithm outperformed traditional techniques like Particle Swarm Optimization (PSO), Genetic Algorithm and Modified Cuckoo Search Algorithm. Background and ObjectivesParkinson's disease is a chronic neurological disorder that directly affects human gait. It leads to slowness of movement, causes muscle rigidity and tremors. Analyzing human gait serves to be useful in studies aiming at early recognition of the disease. In this paper we perform a comparative analysis of various nature inspired algorithms to select optimal features/variables required for aiding in the classification of affected patients from the rest. MethodsFor the experiments, we use a real life dataset of 166 people containing both healthy controls and affected people. Following the optimal feature selection process, the dataset is then classified using a neural network. Results and ConclusionsThe experimental results show Binary Bat Algorithm outperformed traditional techniques like Particle Swarm Optimization (PSO), Genetic Algorithm and Modified Cuckoo Search Algorithm with a competitive recognition rate on the dataset of selected features. We compare this through different criteria like cross-validated accuracies, true positive rates, false positive rates, positive predicted values and negative predicted values.

Breast cancer diagnosis using Artificial Neural Network models

Abstract: Breast cancer is the second leading cause of cancer deaths worldwide and occurrs in one out of eight women. In this paper we develop a system for diagnosis, prognosis and prediction of breast cancer using Artificial Neural Network (ANN) models. This will assist the doctors in diagnosis of the disease. We implement four models of neural networks namely Back Propagation Algorithm, Radial Basis Function Networks, Learning vector Quantization and Competitive Learning Network Experimental results show that Learning Vector Quantization shows the best performance in the testing data set This is followed in order by CL, MLP and RBFN The high accuracy of the LVQ against the other models indicates its better ability for solving the classificatory problem of Breast Cancer diagnosis.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

Abstract: From the Publisher: The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Coverage control for mobile sensing networks

Abstract: This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Artificial neural networks

Abstract: Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.