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E-book: Discrete Problems in Nature Inspired Algorithms

(Indian Institute of Information Technology and Management, Gwalior, India), (Indian Institute of Information Technology and Management, Gwalior, India)
  • Format: 336 pages
  • Pub. Date: 15-Dec-2017
  • Publisher: CRC Press
  • ISBN-13: 9781351260879
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Discrete Problems in Nature Inspired AlgorithmsLarger Image
  • Format: 336 pages
  • Pub. Date: 15-Dec-2017
  • Publisher: CRC Press
  • ISBN-13: 9781351260879
Other books in subject:

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This book includes introduction of several algorithms which are exclusively for graph based problems, namely combinatorial optimization problems, path formation problems, etc. Each chapter includes the introduction of the basic traditional nature inspired algorithm and discussion of the modified version for discrete algorithms including problems pertaining to discussed algorithms.

Reviews

"Each chapter includes detailed problem formulation, practical examples, flowcharts illustrating special algorithms, questions and solved exercises which reinforce important topics. Besides being very useful to those who are interested in discrete optimizations problems and applying various metaheuristics to them, involved reader can also benefit from the easy way it presents various ideas and approaches to problem solutions. It is written in a clean and easily understandable, but still highly scientific language and it is a beneficial reading for post-docs and researchers interested in metaheuristic approaches to graph-based discrete optimization problems." Zentralblatt MATH

Foreword I xv
Foreword II xvii
Foreword III xix
Preface xxi
Acknowledgments xxiii
Authors xxv
1 Introduction to Optimization Problems 1(28)
1.1 Introduction
1(10)
1.1.1 Artificial Intelligence
2(1)
1.1.2 Soft Computing
3(1)
1.1.3 Intelligent Systems
3(1)
1.1.4 Expert Systems
4(1)
1.1.5 Inference Systems
4(1)
1.1.6 Machine Learning
5(1)
1.1.7 Adaptive Learning
5(1)
1.1.8 Related Work Done
6(5)
1.1.8.1 Heuristics
6(1)
1.1.8.2 Meta-Heuristics
6(1)
1.1.8.3 Hyper-Heuristics
7(1)
1.1.8.4 Nature-Inspired Computation
7(2)
1.1.8.5 Multiagent System
9(1)
1.1.8.6 Multiagent Coordination
9(1)
1.1.8.7 Multiagent Learning
10(1)
1.1.8.8 Learning in Uncertainty
10(1)
1.1.8.9 Knowledge Acquisition in Graph-Based Problems Knowledge
10(1)
1.2 Combinatorial Optimization Problems
11(10)
1.2.1 Traveling Salesman Problem
12(1)
1.2.2 Assignment Problem
12(1)
1.2.3 Quadratic Assignment Problem
13(1)
1.2.4 Quadratic Bottleneck Assignment Problem
14(1)
1.2.5 0/1 Knapsack Problem
14(1)
1.2.6 Bounded Knapsack Problem
15(1)
1.2.7 Unbounded Knapsack Problem
15(1)
1.2.8 Multichoice Multidimensional Knapsack Problem
16(1)
1.2.9 Multidemand Multidimensional Knapsack Problem
16(1)
1.2.10 Quadratic Knapsack Problem
16(1)
1.2.11 Sharing Knapsack Problem
17(1)
1.2.12 Corporate Structuring
17(1)
1.2.13 Sequential Ordering Problem
18(1)
1.2.14 Vehicle Routing Problem
18(1)
1.2.15 Constrained Vehicle Routing Problem
18(1)
1.2.16 Fixed Charge Transportation Problem
19(1)
1.2.17 Job Scheduling
19(1)
1.2.18 One-Dimensional Bin Packing Problem
20(1)
1.2.19 Two-Dimensional Bin Packing Problem
20(1)
1.3 Graph-Based Problems
21(1)
1.3.1 Graph Coloring Problem
21(1)
1.3.2 Path Planning Problem
21(1)
1.3.3 Resource Constraint Shortest Path Problem
22(1)
1.4 Aim of This Book
22(1)
1.5 Summary
22(1)
Solved Examples
23(2)
Exercises
25(1)
References
26(3)
2 Particle Swarm Optimization 29(28)
2.1 Introduction
29(1)
2.2 Traditional Particle Swarm Optimization Algorithm
30(1)
2.3 Variants of Particle Swarm Optimization Algorithm
31(17)
2.3.1 Sequential Particle Swarm Optimization Algorithm
32(2)
2.3.1.1 Random Propagation
32(1)
2.3.1.2 Adaptive Schema for Sequential Particle Swarm Optimization Algorithm
33(1)
2.3.1.3 Convergence Criterion
33(1)
2.3.2 Inertia Weight Strategies in Particle Swarm Optimization Algorithm
34(3)
2.3.2.1 Constant Inertia Weight
34(1)
2.3.2.2 Random Inertia Weight
34(1)
2.3.2.3 Adaptive Inertia Weight
34(1)
2.3.2.4 Sigmoid Increasing Inertia Weight
35(1)
2.3.2.5 Sigmoid Decreasing Inertia Weight
35(1)
2.3.2.6 Linear Decreasing Inertia Weight
35(1)
2.3.2.7 The Chaotic Inertia Weight
35(1)
2.3.2.8 Chaotic Random Inertia Weight
36(1)
2.3.2.9 Oscillating Inertia Weight
36(1)
2.3.2.10 Global-Local Best Inertia Weight
36(1)
2.3.2.11 Simulated Annealing Inertia Weight
36(1)
2.3.2.12 Logarithm Decreasing Inertia Weight
37(1)
2.3.2.13 Exponent Decreasing Inertia Weight
37(1)
2.3.3 Fine Grained Inertia Weight Particle Swarm Optimization Algorithm
37(1)
2.3.4 Double Exponential Self-Adaptive Inertia Weight Particle Swarm Optimization Algorithm
38(1)
2.3.5 Double Exponential Dynamic Inertia Weight Particle Swarm Optimization Algorithm
39(1)
2.3.6 Adaptive Inertia Weight Particle Swarm Optimization Algorithm
40(1)
2.3.7 Chaotic Inertial Weight Approach in Particle Swarm Optimization Algorithm
41(1)
2.3.7.1 Application of Chaotic Sequences in Particle Swarm Optimization Algorithm
41(1)
2.3.7.2 Crossover Operation
42(1)
2.3.8 Distance-Based Locally Informed Particle Swarm Optimization Algorithm
42(1)
2.3.8.1 Fitness Euclidean-Distance Ratio Particle Swarm Optimization
42(1)
2.3.9 Speciation-Based Particle Swarm Optimization
43(1)
2.3.10 Ring Topology Particle Swarm Optimization
43(1)
2.3.11 Distance-Based Locally Informed Particle Swarm
43(2)
2.3.12 Inertia-Adaptive Particle Swarm Optimization Algorithm with Particle Mobility Factor
45(1)
2.3.13 Discrete Particle Swarm Optimization Algorithm
46(1)
2.3.14 Particle Swarm Optimization Algorithm for Continuous Applications
47(1)
2.4 Convergence Analysis of Particle Swarm Optimization Algorithm
48(1)
2.5 Search Capability of Particle Swarm Optimization Algorithm
49(1)
2.6 Summary
49(1)
Solved Examples
50(5)
Exercises
55(1)
References
56(1)
3 Genetic Algorithms 57(24)
3.1 Introduction
57(2)
3.2 Encoding Schemes
59(3)
3.2.1 Continuous Value Encoding
59(1)
3.2.2 Binary Encoding
60(1)
3.2.3 Integer Encoding
60(1)
3.2.4 Value Encoding or Real Encoding
61(1)
3.2.5 Tree Encoding
61(1)
3.2.6 Permutation Encoding
61(1)
3.3 Selection
62(2)
3.3.1 Roulette Wheel Selection
63(1)
3.3.2 Rank Selection
63(1)
3.3.3 Tournament Selection
64(1)
3.3.4 Steady-State Selection
64(1)
3.3.5 Random Selection
64(1)
3.4 Crossover
64(2)
3.4.1 Single Point Crossover
64(1)
3.4.2 N Point Crossover
65(1)
3.4.3 Uniform Crossover
65(1)
3.4.4 Arithmetic Crossover
65(1)
3.4.5 Tree Crossover
66(1)
3.4.6 Order Changing Crossover
66(1)
3.4.7 Shuffle Crossover
66(1)
3.5 Mutation
66(2)
3.5.1 Inversion Mutation
67(1)
3.5.2 Insertion Mutation
67(1)
3.5.3 Displacement Mutation
67(1)
3.5.4 Reciprocal Exchange Mutation (Swap Mutation)
68(1)
3.5.5 Shift Mutation
68(1)
3.6 Similarity Template
68(1)
3.7 Building Blocks
69(1)
3.8 Control Parameters
70(1)
3.9 Nontraditional Techniques in GAs
70(2)
3.9.1 Genetic Programming
71(1)
3.9.2 Discrete Genetic Algorithms
71(1)
3.9.3 Genetic Algorithms for Continuous Applications
72(1)
3.10 Convergence Analysis of Genetic Algorithms
72(1)
3.11 Limitations and Drawbacks of Genetic Algorithms
72(1)
3.12 Summary
73(1)
Solved Examples
73(5)
Exercises
78(1)
References
78(3)
4 Ant Colony Optimization 81(14)
4.1 Introduction
81(1)
4.2 Biological Inspiration
81(3)
4.2.1 Competition
82(1)
4.2.2 High Availability
82(1)
4.2.3 Brownian Motion
82(1)
4.2.4 Pheromones and Foraging
83(1)
4.3 Basic Process and Flowchart
84(1)
4.4 Variants of Ant Colony Optimization
85(5)
4.4.1 Ant System
85(1)
4.4.2 Ant Colony Optimization
85(1)
4.4.3 Best-Worst Ant System
86(1)
4.4.4 MAX-MIN Ant System
87(1)
4.4.5 Rank-Based Ant System
87(1)
4.4.6 Ant-Q
87(1)
4.4.7 Hyper Cube Ant System
88(1)
4.4.8 Mean-Minded Ant Colony Optimization Algorithm
88(24)
4.4.8.1 Mathematical Formulations for Mean-Minded Ant Colony Optimization Algorithm
89(1)
4.5 Applications
90(1)
4.6 Summary
91(1)
Solved Examples
91(2)
Exercises
93(1)
References
93(2)
5 Bat Algorithm 95(14)
5.1 Biological Inspiration
95(1)
5.2 Algorithm
95(1)
5.3 Related Work
96(9)
Solved Examples
105(1)
Exercises
106(1)
References
107(2)
6 Cuckoo Search Algorithm 109(18)
6.1 Introduction
109(1)
6.2 Traditional Cuckoo Search Optimization Algorithm
110(2)
6.3 Variants of Cuckoo Search Algorithm
112(6)
6.3.1 Modified Cuckoo Search
113(1)
6.3.2 Improved Cuckoo Search Algorithm with Adaptive Method
113(2)
6.3.3 Multiobjective Cuckoo Search Algorithm for Design Optimization
115(2)
6.3.3.1 Pareto Front
116(1)
6.3.4 Gradient-Based Cuckoo Search for Global Optimization
117(1)
6.4 Applications
118(3)
6.4.1 Recognition of Parkinson Disease
118(1)
6.4.2 Practical Design of Steel Structures
118(1)
6.4.3 Manufacturing Optimization Problems
119(1)
6.4.4 Business Optimization
119(1)
6.4.5 Optimized Design for Reliable Embedded System
119(1)
6.4.6 Face Recognition
120(1)
6.5 Summary and Concluding Remarks
121(1)
Solved Examples
122(1)
Exercises
123(1)
References
124(3)
7 Artificial Bee Colony 127(18)
7.1 Introduction
127(1)
7.2 Biological Inspiration
127(1)
7.3 Swarm Behavior
128(3)
7.3.1 ABC Algorithm
130(1)
7.4 Various Stages of Artificial Bee Colony Algorithm
131(1)
7.5 Related Work
132(9)
Solved Examples
141(1)
Exercises
142(1)
References
143(2)
8 Shuffled Frog Leap Algorithm 145(16)
8.1 Introduction
145(1)
8.2 Related Work Done
146(11)
8.2.1 Discrete Shuffled Flog Leaping Algorithm
150(1)
8.2.2 Quantum Shuffled Frog Leaping Algorithm
151(6)
Solved Questions
157(1)
Unsolved Questions
158(1)
References
159(2)
9 Brain Storm Swarm Optimization Algorithm 161(18)
9.1 Introduction
161(1)
9.2 Brain Storm Optimization
161(3)
9.2.1 Brain Storm Optimization Algorithm
162(2)
9.3 Related Work in Brain Storm Optimization and Other Contemporary Algorithms
164(2)
9.4 Hybridization of Brain Storm Optimization with Probabilistic Roadmap Method Algorithm
166(8)
9.5 Conclusion
174(1)
9.6 Future Scope
174(1)
Solved Examples
174(1)
Exercises
175(1)
References
175(4)
10 Intelligent Water Drop Algorithm 179(18)
10.1 Intelligent Water Drop Algorithm
179(2)
10.1.1 Inspiration and Traditional Intelligent Water Drop Algorithm
179(2)
10.2 Intelligent Water Drop Algorithm for Discrete Applications
181(5)
10.2.1 Intelligent Water Drop Algorithm for an Optimized Route Search
182(3)
10.2.2 Intelligent Water Drop Algorithm Convergence and Exploration
185(1)
10.3 Variants of Intelligent Water Drop Algorithm
186(4)
10.3.1 Adaptive Intelligent Water Drop Algorithm
186(1)
10.3.2 Same Sand for Both Parameters (SC1)
187(1)
10.3.3 Different Sand for Parameters Same Intelligent Water Drop Can Carry Both (SC2)
188(1)
10.3.4 Different Sand for Parameters Same Intelligent Water Drop Cannot Carry Both (SC3)
189(1)
10.4 Scope of Intelligent Water Drop Algorithm for Numerical Analysis
190(1)
10.5 Intelligent Water Drop Algorithm Exploration and Deterministic Randomness
190(1)
10.6 Related Applications
190(3)
10.7 Summary
193(1)
Solved Question
194(1)
Unsolved Questions
194(1)
References
195(2)
11 Egyptian Vulture Algorithm 197(16)
11.1 Introduction
197(1)
11.2 Motivation
197(1)
11.3 History and Life Style of Egyptian Vulture
198(1)
11.4 Egyptian Vulture Optimization Algorithm
199(5)
11.4.1 Pebble Tossing
200(2)
11.4.2 Rolling with Twigs
202(1)
11.4.3 Change of Angle
203(1)
11.4.4 Brief Description of the Fitness Function
204(1)
11.4.5 Adaptiveness of the Egyptian Vulture Optimization Algorithm
204(1)
11.5 Applications of the Egyptian Vulture Optimization Algorithm
204(3)
11.5.1 Results of Simulation of Egyptian Vulture Optimization Algorithm over Speech and Gait Set
204(3)
Exercises
207(1)
Solved Questions
207(4)
References
211(2)
12 Biogeography-Based Optimization 213(18)
12.1 Introduction
213(1)
12.2 Biogeography
214(2)
12.3 Biogeography Based Optimization
216(2)
12.3.1 Migration
216(1)
12.3.2 Mutation
217(1)
12.4 Biogeography-Based Optimization Algorithm
218(1)
12.5 Differences between Biogeography-Based Optimization and Other Population-Based Optimization Algorithm
219(2)
12.6 Pseudocode of the Biogeography-Based Optimization Algorithm
221(2)
12.6.1 Some Modified Biogeography-Based Optimization Approaches
221(2)
12.6.1.1 Blended Biogeography-Based Optimization
221(1)
12.6.1.2 Biogeography-Based Optimization with Techniques Borrowed from Evolutionary Strategies
222(1)
12.6.1.3 Biogeography-Based Optimization with Immigration Refusal
222(1)
12.6.1.4 Differential Evolution Combined with Biogeography-Based Optimization
222(1)
12.7 Applications of Biogeography-Based Optimization
223(3)
12.7.1 Biogeography-Based Optimization for the Traveling Salesman Problem
223(1)
12.7.2 Biogeography-Based Optimization for the Flexible Job Scheduling Problem
224(1)
12.7.3 Biogeography-Based Optimization of Neuro-Fuzzy System; Parameters for the Diagnosis of Cardiac Disease
224(1)
12.7.4 Biogeography-Based Optimization Technique for Block-Based Motion Estimation in Video Coding
225(1)
12.7.5 A Simplified Biogeography-Based Optimization Using a Ring Topology
225(1)
12.7.6 Satellite Image Classification
225(1)
12.7.7 Feature Selection
225(1)
12.8 Convergence of Biogeography-Based Optimization for Binary Problems
226(1)
Solved Questions
226(2)
Unsolved Questions
228(1)
References
229(2)
13 Invasive Weed Optimization 231(14)
13.1 Invasive Weed Optimization
231(2)
13.1.1 Invasive Weed Optimization Algorithm in General
232(1)
13.1.2 Modified Invasive Weed Optimization Algorithm
233(1)
13.2 Variants of Invasive Weed Optimization
233(4)
13.2.1 Modified Invasive Weed Optimization Algorithm with Normal Distribution for Spatial Dispersion
234(3)
13.2.2 Discrete Invasive Weed Optimization in General
237(1)
13.3 Invasive Weed Optimization Algorithm for Continuous Application
237(2)
13.3.1 Invasive Weed Optimization for Mathematical Equations
237(1)
13.3.2 Discrete Invasive Weed Optimization Algorithm for Discrete Applications
238(7)
13.3.2.1 Invasive Weed Optimization Algorithm Dynamics and Search
238(1)
13.3.2.2 Hybrid of IWO and Particle Swarm Optimization for Mathematical Equations
238(1)
13.4 Related Work
239(3)
13.5 Summary
242(1)
Solved Questions
243(1)
Unsolved Questions
243(1)
References
244(1)
14 Glowworm Swarm Optimization 245(26)
14.1 Introduction
245(4)
14.1.1 The Algorithm Description
246(3)
14.2 Variants of Glowworm Swarm Optimization Algorithm
249(12)
14.2.1 Hybrid Coevolutionary Glowworm Swarm Optimization (HCGSO)
249(2)
14.2.1.1 Transformation of the Problem
250(1)
14.2.1.2 The Process of HCGSO
250(1)
14.2.2 Glowworm Swarm Optimization with Random Disturbance Factor
251(1)
14.2.3 Glowworm Swarm Optimization Algorithm Based on Hierarchical Multisubgroup
251(2)
14.2.3.1 Improved Logistic Map
252(1)
14.2.3.2 Adaptive Step Size
252(1)
14.2.3.3 Selection and Crossover
252(1)
14.2.3.4 Hybrid Artificial Glowworm Swarm Optimization Algorithm
253(1)
14.2.4 Particle Glowworm Swarm Optimization
253(3)
14.2.4.1 Parallel Hybrid Mutation
254(1)
14.2.4.2 Local Searching Strategy
254(1)
14.2.4.3 Particle Glowworm Swarm Optimization Algorithm
255(1)
14.2.5 Glowworm Swarm Optimization Algorithm-Based Tribes
256(2)
14.2.5.1 Tribal Structure
256(1)
14.2.5.2 The Glowworm Swarm Optimization Algorithm-Based Tribes
256(2)
14.2.6 Adaptive Neighborhood Search's Discrete Glowworm Swarm Optimization Algorithm
258(3)
14.2.6.1 Adaptive Neighborhood Search's Discrete Glowworm Swarm Optimization Algorithm and Its Application to Travelling Salesman Problem
258(2)
14.2.6.2 Some Other Features of the Algorithm
260(1)
14.2.6.3 Adaptive Neighborhood Search's Discrete Glowworm Swarm Optimization Algorithm Steps
260(1)
14.3 Convergence Analysis of Glowworm Swarm Optimization Algorithm
261(1)
14.4 Applications of Glowworm Swarm Optimization Algorithms
261(4)
14.4.1 Hybrid Artificial Glowworm Swarm Optimization for Solving Multidimensional 0/1 Knapsack Problem
262(1)
14.4.2 Glowworm Swarm Optimization Algorithm for K-Means Clustering
262(1)
14.4.3 Discrete Glowworm Swarm Optimization Algorithm for Finding Shortest Paths Using Dijkstra Algorithm and Genetic Operators
263(9)
14.4.3.1 Labeling Method
263(1)
14.4.3.2 Roulette Selection Strategy
263(1)
14.4.3.3 Single-Point Crossover Strategy
264(1)
14.4.3.4 Mutation Strategy
264(1)
14.4.3.5 Procedure of Glowworm Swarm Optimization Algorithm for Finding Shortest Paths
264(1)
14.5 Search Capability of Glowworm Swarm Optimization Algorithm
265(1)
14.6 Summary
265(1)
Solved Examples
266(2)
Exercises
268(1)
References
269(2)
15 Bacteria Foraging Optimization Algorithm 271(12)
15.1 Introduction
271(1)
15.2 Biological Inspiration
271(1)
15.3 Bacterial Foraging Optimization Algorithm
272(4)
15.3.1 Chemotaxis
274(1)
15.3.2 Swarming
274(1)
15.3.3 Reproduction
275(1)
15.3.4 Elimination and Dispersal
275(1)
15.4 Variants of Bacterial Foraging Optimization Algorithm with Applications
276(3)
Solved Questions
279(2)
Unsolved Questions
281(1)
References
281(2)
16 Flower Pollination Algorithm 283
16.1 Introduction
283(1)
16.2 Flower Pollination
283(2)
16.2.1 Pollination
283(1)
16.2.2 Self-Pollination
284(1)
16.2.3 Cross-Pollination
284(1)
16.3 Characteristics of Flower Pollination
285(1)
16.4 Flower Pollination Algorithm
285(2)
16.5 Multiobjective Flower Pollination Algorithm
287(1)
16.6 Variants of Flower Pollination Algorithm
288(4)
16.6.1 Modified Flower Pollination Algorithm for Global Optimization
288(4)
16.6.2 Elite Opposition-Based Flower Pollination Algorithm
292(1)
16.6.2.1 Global Elite Opposition-Based Learning Strategy
292(1)
16.6.2.2 Local Self-Adaptive Greedy Strategy
292(1)
16.6.2.3 Dynamic Switching Probability Strategy
292(1)
16.7 Application of Flower Pollination Algorithm
292(2)
16.7.1 The Single-Objective Flower Pollination Algorithm
292(1)
16.7.2 The Multiobjective Flower Pollination Algorithm
293(1)
16.7.3 The Hybrid Flower Pollination Algorithm
293(1)
16.8 Conclusion
294(1)
Solved Questions
294(1)
Unsolved Questions
295(1)
References
296(3)
Index
299
Anupam Shukla is currently a professor with ABV-Indian Institute of Information Technology and Management and has a total of 25 years of experience in both teaching and research. He received the Young Scientist Award from the Madhya Pradesh Council of Science & Technology, Bhopal in 1995 and the Gold Medal from Jadavpur University, Kolkata in 1998 for his postgraduate studies. Professor Shukla's main research area is Artificial Intelligence, and he is currently focusing on Neural Networks and Evolutionary and Nature Inspired Computations that have incalculable applications in Bioinformatics, Medical Expert System, and Robotics. He has supervised 8 PhD students and 67 M Tech thesis in this area. Professor Shukla has published 161 research papers in various national and international journals/conferences, 7 book chapters, and edited two books in the area of biomedical engineering from IGI Global Publishers. Additionally, he has also authored three books entitled: "Real Life Applications of Soft Computing" CRC Press, Taylor and Francis; "Intelligent Planning for Mobile Robotics: Algorithmic Approaches" IGI Global; and "Towards Hybrid and Adaptive Computing: A Perspective", Springer Verlag Publishers.

Ritu Tiwari is an Associate Professor (Department of Information and Communications Technology) at ABV-IIITM, Gwalior. She has 14 years of teaching and research experience which includes 10 years of post PhD Teaching and research Experience. Her field of research includes Robotics, Artificial Intelligence, Soft Computing and Applications (Biometrics, Biomedical, Prediction). She has two patents to her name and has authored three books titled: "Real Life Applications of Soft Computing", Taylor and Francis; "Intelligent Planning for Mobile Robotics: Algorithmic Approaches", IGI Global and "Towards Hybrid and Adaptive Computing: A Perspective", Springer-Verlag Publishers. She has also edited two books in the area of biomedical engineering from IGI Global . She has supervised 5 Ph.D. and 90 masters students and has published 104 research papers in various national and international journals/conferences. She has received Young Scientist Award from Chhattisgarh Council of Science & Technology in the year 2006. She has also received Gold Medal in her post graduation from NIT, Raipur. She has completed ten prestigious research projects sponsored by Department of science and technology (DST) and Department Information Technology (DIT), Government of India. She is currently involved with the Government of India and is working on three sponsored research projects. She is a reviewer of various international journals including ACM Computing Review, IEEE Transactions on Information Technology in Biomedicine, Elsevier Journal of Biomedical Informatics and Elsevier Neurocomputing journal.
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