Srikanta Patnaik

Soft Computing Techniques in Vision Science

This Special Edited Volume is a unique approach towards Computational solution for the upcoming field of study called Vision Science. From a scientific firmament Optics, Ophthalmology, and Optical Science has surpassed an Odyssey of optimizing configurations of Optical systems, Surveillance Cameras and other Nano optical devices with the metaphor of Nano Science and Technology. Still these systems are falling short of its computational aspect to achieve the pinnacle of human vision system. In this edited volume much attention has been given to address the coupling issues Computational Science and Vision Studies. It is a comprehensive collection of research works addressing various related areas of Vision Science like Visual Perception and Visual system, Cognitive Psychology, Neuroscience, Psychophysics and Ophthalmology, linguistic relativity, color vision etc. This issue carries some latest developments in the form of research articles and presentations. The volume is rich of contents with technical tools for convenient experimentation in Vision Science. There are 18 research papers having significance in an array of application areas. The volume claims to be an effective compendium of computing developments like Frequent Pattern Mining, Genetic Algorithm, Gabor Filter, Support Vector Machine, Region Based Mask Filter, 4D stereo camera systems, Principal Component Analysis etc. The detailed analysis of the papers can immensely benefit to the researchers of this domain. It can be an Endeavour in the pursuit of adding value in the existing stock of knowledge in Vision Science.

An enhanced classifier fusion model for classifying biomedical data

Classification is a technique where we discover the hidden class level of the unknown data. As different classification methods produces different accuracy according to the class level; classifier fusion is the solution to achieve more accuracy in every level of the input data. Selection of a suitable classifier in classifier fusion is a tedious task. In the proposed model, the output of the three classifiers is fed to the dynamic classifier fusion technique. This model will use each classifier for every individual data. We have used principal component analysis (PCA) to deal with issues of high dimensionality in biomedical classification. Three types of classification techniques on microarray data like multi layer perceptron (MLP), FLANN and PSO-FLANN have been implemented and compared; it has been observed that MLP is showing better result. We have also proposed a model for classifier fusion, where the model will choose the relevant classifiers according to the different region of datasets.

A Comparative Analysis on Edge Detection of Colloid Cyst: A Medical Imaging Approach

Image processing has a great impact in the field of medical science. The engineering application spreads over various applications and equally it shows the effective performance. In current research, the medical diagnosis as well as the medical data analysis is most challenging job, as it is very complex task. The complexity is tried to reduced by the help of image processing in this approach. Colloidal Cyst, located in the third ventricle of the human brain is considered in this work for the purpose of detection at the time of diagnosis. Image Processing especially useful for detection, recognition and classification etc. In this chapter, a simple as well as a novel method has been applied for the colloidal cyst detection. The novelty is the structuring element is considered in such a manner that a better result is obtained as compared to traditional and basic morphological methods. The structuring elements used as gradient operator and also has been considered in their complementary forms which produces better results than the initial structuring elements.

Design of Intelligent Transportation System Supported by New Generation Wireless Communication Technology

The development of Intelligent Transportation System (ITS) will transition into a next level development of variously new requirements coupled with the new generation wireless communication technology. In this paper, the process-oriented structured system analysis is extended on the existing intelligent transportation architecture to establish an intelligent transportation system based on the new generation wireless communication technology, and further in-depth analysis will be performed to discuss the required additional capabilities of network, services and application support in the view of user, function, information, connection and communication. In order to comprehensively understand the technical characteristics and development of the new generation intelligent transportation system, it lays a foundation for implementation of the new generation ITS technology. Design of Intelligent Transportation System Supported by New Generation Wireless Communication Technology

Dynamic routing algorithm in wireless mesh network

Wireless mesh networks (WMNs) have attracted increasing attention and deployment as a high-performance and low-cost solution to last-mile broadband internet access. WMNs have been broadly accepted in the conventional application sectors of ad hoc networks because of their advantages over other wireless networks. Traffic routing has a critical role in determining the performance of wireless mesh networks. Routing protocol design for wireless mesh networks is critical to maintain the performance and reliability of wireless mesh networks. Thus a routing protocol or an algorithm for WMNs should be carefully designed taking into account the specific characteristics of that network. Traditional routing protocols send traffic along pre-determined paths and have been shown ineffective in coping with unreliable and unpredictable wireless medium. In this paper we propose to formulate the mesh network routing problem as an optimisation problem. In the formulation of the problem we have considered both the static and dynamic traffic demand. This paper proposes a dynamic routing algorithm that is able to provide the optimal performance on all possible traffic demands. Our simulation results show that the proposed method performs competitively.

Text-to-speech synthesis with an Indian language perspective

Speech synthesis deals with artificial production of speech, and a Text-to-Speech TTS system in this aspect converts natural language text into a corresponding spoken waveform or speech. There have been sufficient successes today in the area of speech and natural language processing that suggest that these technologies will continue to be a major area of research and development in creating intelligent systems. In this paper we provide an overview of the TTS synthesis technology along with details of the phases involved. The thrust has been given to explore the usefulness of this technique in designing a TTS system for Indian languages. This paper also focuses on some of the open research issues where work in this area may further be done.

Software Quality Estimation using Machine Learning: Case-based Reasoning Technique

Software quality estimation is one of the most interesting research areas in the domain of software engineering for last few decades. Large numbers of techniques and models have already been worked out in the area of error estimation. The aim of software quality estimation is to identify error prone tasks as the cost can be minimized with advance knowledge about the errors and this early treatment of error will enhance the software quality. In this paper we have explored a set of data in university setting. This paper advocates the use of case-based reasoning (i.e., CBR) to make a software quality estimation system by the help of human experts. CBR relies on historical information from similar past projects, whereby similarities are determined by comparing the projects, and key attributes. We have used different similarity measures to find the best method which increases estimation accuracy & reliability. This paper presents a work in which we have expanded our previous work [24]. The software is a console based application and thus does not use the GUI functions of the Operating System, which makes it very fast in execution. In order to obtain results we have used an indigenous tool for software quality estimation, run in c++ compiler. General Terms Software Engineering: Quality

Design of ANFIS Based E-Health Care System for Cardio Vascular Disease Detection

As the society is becoming superior day-by- day, loads of smart devices are used in different application areas. This is the challenge to the technocrats for forming the intelligent and smart social systems. It requires easy access and fast processing, which is the main focus of any application. In this work, an attempt has been taken into consideration to develop an intelligent e-healthcare system. In e-healthcare system the entities are considered as the patient, the physician, the pathological centre and result as diagnosis, treatment and post care. This paper uses an ANFIS structure for e-healthcare system. Further the ANFIS system is used for disease diagnosis and support to the patient as well as for physicians. For the management of multi-agent system has been satisfied by, using rule based fuzzy parameters. The service can be provided through internet to the patient as well as by the physician. The different situation of patient automatically informs to the doctor similarly the prescription from the doctor for diagnosis can inform to the pathology centre and vice versa. The result of detection communicated to both for desired medicine, monitoring and post care purpose. The performance found to be excellent to satisfy this part of intelligent system.

Enhancing the Accuracy of Case-Based Estimation Model through Early Prediction of Error Patterns

The paper tries to explore the importance of software fault prediction and to minimize them thoroughly with the advanced knowledge of the error-prone modules, so as to enhance the software quality. For estimating a new project effort, case-based reasoning is used to predict software quality of the system by examining a software module and predicting whether it is faulty or non faulty. In this research we have proposed a model with the help of past data which is used for prediction. Two different similarity measures namely, Euclidean and Manhattan are used for retrieving the matching case from the knowledge base. These measures are used to calculate the distance of the new record set or case from each record set stored in the knowledge base. The matching case(s) are those that have the minimum distance from the new record set. This can be extended to variety of system like web based applications, real time system etc. In this paper we have used the terms errors and faults, and no explicit distinction made between errors and faults. In order to obtain results we have used MATLAB 7.10.0 version as an analyzing tool.

Detection of abnormal cardiac condition using fuzzy inference system

The objective of this paper is to design an intelligent fuzzy inference system for detection of abnormal cardiac condition utilising four parameters such as ECG, blood pressure, blood sugar and cholesterol. The system designed using Sugeno defuzzification method. The system has been implemented by using MIT-BIH repository and MATLAB. And the result obtained out of this simulation shows efficiency better than 95%.