Wavelet Transform-Based Soft Computational Techniques and Applications in Medical Imaging

The aim of this chapter is to highlight the biomedical applications of wavelet transform based soft computational techniques i.e. wavenet and corresponding research efforts in imaging techniques. A brief introduction of wavelet transform, its properties that are vital for biomedical applications touched by various researchers and basics of neural networks has been discussed. The concept of wavelon and wavenet is also discussed in detail. Recent survey of wavelet based neural networks in medical imaging is another facet of this script, which includes biomedical image denoising, image enhancement and functional neuro-imaging, including positron emission tomography and functional MRI.

Behavioral Study of Drosophila Fruit Fly and Its Modeling for Soft Computing Application

Most of the problems arise in real-life situation are complex natured. The level of the complexity increases due to the presence of highly non-linear constraints and increased number of decision variables. Finding the global solution for such complex problems is a greater challenge to the researchers. Fortunately, most of the time, bio-inspired techniques at least provide some near optimal solution, where the traditional methods become even completely handicapped. In this chapter, the behavioral study of a fly namely ‘Drosophila' has been presented. It is worth noting that, Drosophila uses it optimized behavior, particularly, when searches its food in the nature. Its behavior is modeled in to optimization and software is designed called Drosophila Food Search Optimization (DFO).The performance, DFO has been used to solve a wide range of both unconstrained and constrained benchmark function along with some of the real life problems. It is observed from the numerical results and analysis that DFO outperform the state of the art evolutionary techniques with faster convergence rate.

Performance Analysis of DE over K-Means Proposed Model of Soft Computing

In real world data increased periodically, huge amount of data is called Big data. It is a well-known term used to define the exponential growth of data, both in structured and unstructured format. Data analysis is a method of cleaning, altering, learning valuable statistics, decision making and advising assumption with the help of many algorithm and procedure such as classification and clustering. In this chapter we discuss about big data analysis using soft computing technique and propose how to pair two different approaches like evolutionary algorithm and machine learning approach and try to find better cause.

Optimization of Dairy Feeding Models with C-SOMGA

This chapter presents a self-organizing migrating genetic algorithm(C-SOMGA) for animal diet formulation. Bi-objective models for cost minimization and shelf life maximization are developed and objectives are achieved by combination of linear and C-SOMGA. Self-organizing migrating genetic algorithm provides exact and quick solution and an innovative approach towards successful application of soft computing technique in the area of animal diet formulation.

A Novel Mutation Strategy for Differential Evolution

Differential Evolution (DE) has attained the reputation of a powerful optimization technique that can be used for solving a wide range of problems. In DE, mutation is the most important operator as it helps in generating a new solution vector. In this paper we propose an additional mutation strategy for DE. The suggested strategy is named DE/rand-best/2. It makes use of an additional parameter called guiding force parameter K, which takes a value between (0,1) besides using the scaling factor F, which has a fixed value. De/rand-best/2 makes use of two difference vectors, where the difference is taken from the best solution vector. One vector difference will be produced with a randomly generated mutation factor K (0,1). It will add a different vector to the old one and search space will increase with a random factor. Result shows that this strategy performs well in comparison to other mutation strategies of DE.

Determination of Bearing Capacity of Shallow Foundation Using Soft Computing

The ultimate bearing capacity is an important criterion for the successful implementation of any geotechnical projects. This chapter studies the feasibility of employing Gaussian process regression (GPR), Extreme learning machine (ELM) and Minimax probability machine regression (MPMR) for prediction of ultimate bearing capacity of shallow foundation based on cohesionless soils. The developed models have been compared on the basis of coefficient of relation (R) values (GPR= 0.9625, ELM= 0.938, MPMR= 0.9625). The results show that MPMR is more efficient tool but the models of GPR and ELM also gives satisfactory results.

MCOQR (Misuse Case-Oriented Quality Requirements) Metrics Framework

In today's information age, software is attacked deliberately resulting in breach of security & people's trust. These malicious attacks provide harm to individuals, organizations, and the world at large. The attacker targets vulnerabilities to exploit the software. The chapter highlights the importance of security metrics which is comprehensive in nature and easily implementable. It also emphasis on the early implementation of security metrics i.e., from the requirements elicitation phase of requirement engineering stage so that a comprehensive and complete set of security requirements could be defined with their countermeasure to develop a secured software. For development of security metrics a framework has been proposed using use case and misuse case tree modeling. The proposed work may help the Software Security Team to identify and analyse the potential vulnerabilities and associated threats which may be exploited by the attacker to harm the system well in advance in the requirements engineering phase thereby balancing the security using misuse cases modeling.

Comparative Study of GRA and MOORA Methods

This chapter present the study of comparative assessment of Grey Relational Analysis (GRA) method and Multi Objective Optimization on the basis of Ratio Analysis (MOORA) method with considering two distinct weight determination methods named Analytical Hierarchy Process (AHP) method and Entropy method for ranking and selection of Two For One (TFO) machine used in Textile industry. TFO machines are used in textile industry to improve the properties of yarn by twisting. The ranking performance of GRA method and MOORA method is compared with each other with reference to ranking order obtained using different weight determination method and it explore effectiveness and simplicity of MOORA method for selection of best TFO machine.

Hookes-Jeeves-Based Variant of Memetic Algorithm

Due to their wide applicability and easy implementation, Genetic algorithms (GAs) are preferred to solve many optimization problems over other techniques. When a local search (LS) has been included in Genetic algorithms, it is known as Memetic algorithms. In this chapter, a new variant of single-meme Memetic Algorithm is proposed to improve the efficiency of GA. Though GAs are efficient at finding the global optimum solution of nonlinear optimization problems but usually converge slow and sometimes arrive at premature convergence. On the other hand, LS algorithms are fast but are poor global searchers. To exploit the good qualities of both techniques, they are combined in a way that maximum benefits of both the approaches are reaped. It lets the population of individuals evolve using GA and then applies LS to get the optimal solution. To validate our claims, it is tested on five benchmark problems of dimension 10, 30 and 50 and a comparison between GA and MA has been made.

A Cultivated Variant of Differential Evolution Algorithm for Global Optimization

Differential Evolution (DE) algorithm is known as robust, effective and highly efficient for solving the global optimization problems. In this chapter, a modified variant of Differential Evolution (DE) is proposed, named Cultivated Differential Evolution (CuDE) which is different from basic DE in two ways: 1) the selection of the base vector for mutation operation, 2) population generation for the next generation. The performance of the proposed algorithm is validated on a set of eight benchmark problems taken from literature and a real time molecular potential energy problem. The numerical results show that the proposed approach helps in formulating a better trade-off between convergence rate and efficiency. Also, it can be seen that the performance of DE is improved in terms of number of function evaluations, acceleration rate and mean error.