Babita Pandey

Knowledge and intelligent computing system in medicine

Knowledge-based systems (KBS) and intelligent computing systems have been used in the medical planning, diagnosis and treatment. The KBS consists of rule-based reasoning (RBR), case-based reasoning (CBR) and model-based reasoning (MBR) whereas intelligent computing method (ICM) encompasses genetic algorithm (GA), artificial neural network (ANN), fuzzy logic (FL) and others. The combination of methods in KBS such as CBR-RBR, CBR-MBR and RBR-CBR-MBR and the combination of methods in ICM is ANN-GA, fuzzy-ANN, fuzzy-GA and fuzzy-ANN-GA. The combination of methods from KBS to ICM is RBR-ANN, CBR-ANN, RBR-CBR-ANN, fuzzy-RBR, fuzzy-CBR and fuzzy-CBR-ANN. In this paper, we have made a study of different singular and combined methods (185 in number) applicable to medical domain from mid 1970s to 2008. The study is presented in tabular form, showing the methods and its salient features, processes and application areas in medical domain (diagnosis, treatment and planning). It is observed that most of the methods are used in medical diagnosis very few are used for planning and moderate number in treatment. The study and its presentation in this context would be helpful for novice researchers in the area of medical expert system.

An integrated intelligent computing model for the interpretation of EMG based neuromuscular diseases

Intelligent computing system (ICS) and knowledge-based system (KBS) have been widely used in the detection and interpretation of EMG (electromyography) based diseases. Heuristic-based detection methods of EMG parameters for a particular disease have also been reported in the literature but little effort has been made by researchers to combine rule-based reasoning (RBR) and case-based reasoning of KBS, and ANN (artificial neural nets) of ICS. Integrating the methods in KBS and ICS improves the computational and reasoning efficiency of the problem-solving strategy. We have developed an integrated model of CBR and RBR for generating cases, and ANN for matching cases for the interpretation and diagnosis of neuromuscular diseases. We have hierarchically structured the neuromuscular diseases in terms of their physio-pyscho (muscular, cognitive and psychological) parameters and EMG based parameters (amplitude, duration, phase etc.). Cumulative confidence factor is computed at different node from lowest to highest level of hierarchal structure in the process of diagnosis of the neuromuscular diseases. The diseases considered are Duchenne muscular dystrophy, Polymyostits, Endocrine myopathy, Metabolic myopathy, Neuropathy, Poliomyletis and Myasthenia gravis. The basic objective of this work is to develop an integrated model of RBR, CBR and ANN in which RBR is used to hierarchically correlate the sign and symptom of the disease and also to compute cumulative confidence factor (CCF) of the diseases. CBR is used for diagnosing the neuromuscular diseases and to find the relative importance of sign and symptoms of a diseases to other diseases and ANN is used for matching process in CBR.

Knowledge and intelligent computing methods in e-learning

E-learning is the use of technology that enables people to learn at anytime from anywhere. Various single knowledge-based methods KBM such as rule-base reasoning RBR and case-base reasoning CBR; and intelligent computing methods ICM such as genetic algorithm GA, particle swarm optimisation PSO, artificial neural network ANN, multi-agent systems MAS, ant colony optimisation ACO, fuzzy logic FL etc. Integrated KBM-ICM methods such as GA-CBR, ANN-RBR, GA-Ontology and ANN-Mining have been used in various e-learning contexts such as: the learning path generation, adaptive course sequencing and personalisation of recommended learning object etc. We have made a study of different individual KBM and ICM methods; and integrated KBS-ICM methods applicable to e-learning domain right from the mid 1990s to 2014. The study is presented in a tabular form, showing the KBM-ICM methods, e-learning problems to be addressed, specific features and the implementation in the e-learning domain. From the results, it is observed that a single KBM is not deployed to solve any e-learning problem. A single ICM and integrated KBM-ICM methods are used to solve various e-learning problems. The study and its presentation in the context help the novice researchers to resume their work in the area of e-learning systems.

Intelligent techniques and applications in liver disorders: a survey

Liver disease is one of the leading causes of mortality in India, as it is in rest of the world. This paper presents a survey on intelligent techniques applied to liver disorders between the years January 1995 and January 2013. Individual ITs include artificial neural network (ANN), data mining (DM), fuzzy logic (FL) etc. Integrated ITs combine methods as artificial neural network-case-based reasoning (ANN-CBR), artificial immune system-artificial neural network-fuzzy logic (AIS-ANN-FL) etc. The different types of liver disorders covered in the study are hepatitis, liver fibrosis, liver cirrhosis, liver cancer, fatty liver, liver disorders data set, hepatitis data set and hepatobiliary disorders data set. The study identifies which ITs are applied for what types of liver disorders and on which types of disorders maximum works has been done. Another imperative fact emerging from this survey is that large part of the research work on liver disorders has been done from 2007 onwards.

An integrated intelligent computing method for the detection and interpretation of ECG based cardiac diseases

Intelligent computing system and knowledge-based system have been widely used in the diagnosis and classification of ECG based diseases. Several detection methods of ECG parameters for a particular disease have also been reported in the literature. But little effort has been made by researchers to combine both. In this work, an integrated model of rule base system for generating cases and ANN methods for matching cases in the case base reasoning model for the interpretation and diagnosis of sinus disturbances (SD) is developed. The SD is hierarchically structured in terms of their physio-psycho parameters and ECG based parameters. Cumulative confidence factor (CCF) is computed at different nodes of hierarchy. The SD considered are sinus arrest, sinus bradycardia, sinus tachycardia and sinus arrhythmia. MIT/BIH ECG database is used in the simulation study. The basic objective of this work is to enhance the computational effort with certain level of efficiency and accuracy.

A novel method of case representation and retrieval in CBR for e-learning

In this paper we have discussed a novel method which has been developed for representation and retrieval of cases in case based reasoning (CBR) as a part of e-learning system which is based on various student features. In this approach we have integrated Artificial Neural Network (ANN) with Data mining (DM) and CBR. ANN is used to find the relationship between student characteristics and learning performance, DM to generate classification rules for learning outcomes which are further used to generate cases for the case base and CBR for reasoning. This adaptive system helps in facilitating the course content of different difficulty level to individuals according to their features. The result shows the above method provides the learning material to student as per their need and helps them to enhance their learning.

Two Level Diagnosis of Breast Cancer using Data Mining

Cancer is a dreadful disease. Mostly women affected with breast cancer disease. Mainly problem in medical science is to diagnosis of breast cancer at early stage. So the early detection of breast cancer is important for saving life. In this work, develop method for diagnosis of breast cancer at two levels. At the first level diagnosis is based Wisconsin Breast Cancer dataset (pathological test result) and classified into malignant and benign class. At the second level diagnosis based on pathological and physiological parameters of malignant breast cancer dataset and classified into five breast cancer disease as: Ductal Carcinoma in Situ(DCIS), Lobular Carcinoma in Situ(LCIS), Invasive Ductal Carcinoma(IDC), Invasive Lobular Carcinoma(ILC) and Mucinous Carcinoma(MC). In this paper evaluate the performance based on correct and incorrect element of data classification using J48 classification algorithm. The experiment result shows that classification accuracy, sensitivity and specificity of J48 is good.

Architecture based Comparison of Semantic Web Service Composition Processes

ABSTRACT Semantic web services development become rapidly increased as dynamic changes are occurred. Various approaches are adopted to develop composite service systematically. This paper aims to make development process easier by classifying the literature on web services composition based approaches like selection, discovery, orchestration, choreography, mediation, automatic composition to facilitate the end to end semantic web service composition easier. Applying semantics in web process cycle helps to address critical issues in reuse, integration and scalability. In order to find best approach, various composition approaches on these requirements were evaluated and suggestions were provided on what approach can be used in which scenario to achieve best results. Keywords Semantic web service, Service Composition approaches, Selection, Discovery, Orchestration, Choreography. 1. INTRODUCTION Semantic web is an extension of current web, in which information is given well defined meaning, better enabling computer and people to work in cooperation [1]. The objective of used to find services available for clients input request and research around semantic web service is to facilitate automatic handling of web services. Semantic web transforms the web into repository of computer readable data, while web services provide tool for automatic reuse of that data. Sometimes single service component unable to satisfy user needs then some mechanisms like discovery [4], selection [18], mediation [12], matchmaking etc. are used for finding service component and semantic composition helps to aggregate the component of various services according to tasks. In previous work we have made a survey on different approaches of semantic web services [18]. The main objective of this paper is to discuss some existing composition techniques like Workflow based, Artificial Planning, Context based, Agent based, Ontology based, Orchestration and Choreography to solve semantic web composition problem with their attributes. In addition, we have also classified and compared different web service composition methods. Apart from introduction part, section 2 deals with various composition based method for semantic web service, section 3 covers framework of service composition methods, section 4 covers classification and comparison among several approaches and conclusion in section 5.

Blended e-Learning Training (BeLT): Enhancing Railway Station Controller Knowledge

With the growing economy, e-learning consequently gained increasing attention as it conveys knowledge globally with improved interactivity, assistance and reduced costs. For the past few years, accidental challenges have become the severe problem with railway units due to irresponsibility, lack of knowledge and improper guidance of station controllers (learners). While focusing on e-learning technologies railway units failed to admit learners need, cultural diversity and background skills by creating ethnically impartial e-learning environments, which resulted in inadequate training and degraded performance. The purpose of this study is to understand the vision of a global diverse group of station traffic controllers about e-learning courses developed by their individual railway units. The opinions of these officials have been verified by questionnaires on the basis of course organization, course accuracy, course effectiveness, course relevance, course productivity and course interactivity. The results obtained show that the developed e-learning course was highly helpful, interactive, creative, and user-friendly for learners. This leads to making e-learning conquered among independent learners.

Performance analysis on agriculture ontology using SPARQL query system

Ontologies are used to represent domain knowledge with help of object, their behaviour and properties. This paper represents web enabled approach on agriculture semantic web using SPARQL and specified tools to increase productivity of farmers. This work focuses on assessment of query optimization tools and results predicted from them to determine the suitability of each method for different users where structured ontologies are used as querying aids for agriculture based dataset.