Prabhat Mahanti

Differential Evolution for learning the classification method PROAFTN

This paper introduces a new learning technique for the multicriteria classification method PROAFTN. This new technique, called DEPRO, utilizes a Differential Evolution (DE) algorithm for learning and optimizing the output of the classification method PROAFTN. The limitation of the PROAFTN method is largely due to the set of parameters (e.g., intervals and weights) required to be obtained to perform the classification procedure. Therefore, a learning method is needed to induce and extract these parameters from data. DE is an efficient metaheuristic optimization algorithm based on a simple mathematical structure to mimic a complex process of evolution. Some of the advantages of DE over other global optimization methods are that it often converges faster and with more certainty than many other methods and it uses fewer control parameters. In this work, the DE algorithm is proposed to inductively obtain PROAFTNs parameters from data to achieve a high classification accuracy. Based on results generated from 12 public datasets, DEPRO provides excellent results, outperforming the most common classification algorithms.

An evolutionary framework using particle swarm optimization for classification method PROAFTN

Abstract: The aim of this paper is to introduce a methodology based on the particle swarm optimization (PSO) algorithm to train the Multi-Criteria Decision Aid (MCDA) method PROAFTN. PSO is an efficient evolutionary optimization algorithm using the social behavior of living organisms to explore the search space. It is a relatively new population-based metaheuristic that can be used to find approximate solutions to difficult optimization problems. Furthermore, it is easy to code and robust to control parameters. To apply PROAFTN, the values of several parameters need to be determined prior to classification, such as boundaries of intervals and weights. In this study, the proposed technique is named PSOPRO, which utilizes PSO to elicit the PROAFTN parameters from examples during the learning process. To test the effectiveness of the methodology and the quality of the obtained models, PSOPRO is evaluated on 12 public-domain datasets and compared with the previous work applied on PROAFTN. The computational results demonstrate that PSOPRO is very competitive with respect to the most common classification algorithms.

Automatic parameter settings for the PROAFTN classifier using hybrid particle swarm optimization

In this paper, a new hybrid metaheuristic learning algorithm is introduced to choose the best parameters for the classification method PROAFTN PROAFTN is a multi-criteria decision analysis (MCDA) method which requires values of several parameters to be determined prior to classification These parameters include boundaries of intervals and relative weights for each attribute The proposed learning algorithm, identified as PSOPRO-RVNS as it integrates particle swarm optimization (PSO) and Reduced Variable Neighborhood Search (RVNS), is used to automatically determine all PROAFTN parameters The combination of PSO with RVNS allows to improve the exploration capabilities of PSO by setting some search points to be iteratively re-explored using RVNS Based on the generated results, experimental evaluations show that PSOPRO-RVNS outperforms six well-known machine learning classifiers in a variety of problems.

Discretization Techniques and Genetic Algorithm for Learning the Classification Method PROAFTN

This paper introduces new techniques for learning the classification method PROAFTN from data. PROAFTN is a multi-criteria classification method and belongs to the class of supervised learning algorithms. To use PROAFTN for classification, some parameters must be obtained for this purpose. Therefore, an automatic method to extract these parameters from data with minimum classification errors is required. Here, discretization techniques and genetic algorithms are proposed for establishing these parameters and then building the classification model. Based on the obtained results, the newly proposed approach outperforms widely used classification methods.

Soft Modeling of Group Dynamics and Behavioral Attributes

Social networks, religion and culture of human beings play a major role in the day-to-day activities performed by each individual in group oriented missions. The aggregation and inertia in the group are typically important to achieve the goal. A leader being the most dominant and knowledgeable, with leadership qualities, steers movements, thought processes, and actions of the individuals of his/her group. However the psychology of each individual is unique. This complex behavior is often observed in the software development projects, where the cognitive attributes and contribution of programmer’s mind are some of the important features to develop a project. This paper proposes a model for the behavior of programmers (software developers) in a development project by incorporating fuzzy logic as a tool. The implication of this model also assists in gaining substantial information about the learning environment of the programmer during the actual implementation and post session o f the project and at the same time also helps to evolve the concept of Virtual Project Leader (VPL) for similar projects.

M-Cloud Computing Based Agriculture Management System

Modernization in agriculture sector is one of the major challenging problems in India. Currently, Indian farmers faced many problems in agriculture domain such as lack of irrigation infrastructure, market infrastructure and transport infrastructure along the presence of a chain of middlemen through whom most agricultural commodities must circulate before finally reaching consumers etc. One of the possible solutions for improvement is by using mobile applications help in gathering information from farmers such location-based information and environmental. This chapter presents a framework to solve the problems in agriculture by using Mobile based Cloud computing platform which makes smart farmers and increases the productivity. This framework promotes a fast development of agricultural modernization, realize smart agriculture and effectively solve the problems concerning agriculture, countryside, and farmers.

Intelligent computational techniques

This guest editorial introduces the special issue on “Intelligent Computational Techniques”. The goal of this special issue was to solve a variety of real-life problems which have uncertainty, imprecision, vagueness, resulting in high performance applications or prototypes for real time system. The special issue touched different hot topics related to Computer Vision, Computational Biology, Natural Language Processing, Computer Networks, Software Engineering, Industrial Production and Big Data.

Web query reformulation using differential evolution

This paper presents a query reformulation and clustering technique using Differential Evolution. Differential evolution (DE) has emerged as one of the fast, robust, and efficient global search heuristics of current interest. The proposed DE automatically determines the type of a query and new pattern of query reformulation.

Simulation of digital control systems

A computer technique for simulating digital control systems is presented. It is conceived with recursion formulas to describe the interconnected elements of the system, those of the digital and the continuous elements, in a unified fashion. The technique permitted experimentation with different combinations of system parameters and system configurations. The efficiency of the techniques suggest its suitability for a rational engineering design of a control system before implementation.