July 2011 Issue Vol.1 No.7
# Surface Robotics Lab, Central Mechanical Engineering Research Institute (CSIR) Durgapur, W.B. 713209, India
1 firstname.lastname@example.org 2 email@example.com
* Department of Mechanical Engineering, National Institute of Technology Durgapur, W.B. 713209, India
Abstract: Abstract — The use of mobile robots is being popular over the world mainly for autonomous explorations in hazardous/ toxic or unknown environments. This exploration will be more effective and efficient if the explorations in unknown environment can be aided with the learning from past experiences. Currently reinforcement learning is getting more acceptances for implementing learning in robots from the system-environment interactions. This learning can be implemented using the concept of both single-agent and multiagent. This paper describes such a multiagent approach for implementing a type of reinforcement learning using a priority based behaviour-based architecture. This proposed methodology has been successfully tested in both indoor and outdoor environments.
Keywords: Keywords: Multiagent, Reinforcement learning, Q-learning, Behavior-based robotics, Autonomous exploration
# Assistant Professor, Department of Computer Science and Engineering, Bharathidasan University, Tamilnadu, India
1 firstname.lastname@example.org * Vice-chancellor, Bharathidasan University, Tamilnadu, India
This paper illustrates a hybrid prediction system consists of Rough Set Theory (RST) and Artificial Neural Network (ANN) for processing medical data. In the process of developing a new data mining technique and software to aid efficient solutions for medical data analysis, we propose a hybrid tool that incorporates RST and ANN to make efficient data analysis and suggestive predictions. In the experiments, we used spermatological data set for predicting quality of animal semen. The data set used in the experiments is subjected to quantize and normalize, and use this as a reflection of the internal system state. The RST is used as a tool for reducing and choosing the most relevant sets of internal states for predicting the semen fertilization potential. Chosen optimal data set is input to constructed neural network with supervised learning algorithm for the prediction of semen quality. This paper demonstrates that the RST is an effective pre-processing tool for reducing the number of input vector to ANN without reducing the basic knowledge of the information system in order to increase prediction accuracy of the proposed system. The resulting system is a hybrid prediction system for medical database called an Intelligent Rough Neural Network System (IRNNS).
Keywords: Artificial Neural Network, Machine learning technique, In-vitro fertilization, Rough sets theory (RST), Fertility rate prediction, IRNNS, Hybrid prediction system.
Flow Of Herschel – Bulkley Fluid In An Inclined Flexible Channel Lined With Porous Material Under PeristalsisS.Sreenadh 1, S.Rajender #1, S.V.H.N.Krishna Kumari *2,Y.V.K.Ravi Kumar $3
1 Department of Mathematics, Sri Venkateswara University, Tirupati, INDIA.
2 Department of Mathematics, Bhoj Reddy Engineering College for women, Hyderabad, INDIA.
3 Department of Mathematics, Stanley College of Engineering and Technology women, Hyderabad, INDIA. (Corresponding author, email@example.com)
Abstract— This paper is concerned with the study of the peristaltic flow of Herschel – Bulkley fluid in an inclined flexible channel lined with porous material under long wave length and low Reynolds number assumptions. This model may be applicable to describe blood flow in the sense that erythrocytes region and the plasma regions may be described as plug flow and non-plug flow regions. The effect of yield stress , Darcy number ,angle of inclination and the index on the flow characteristics is discussed through graphs.
Keywords: peristaltic flow,Herschel – Bulkley fluid, inclined channel.
School of Computer Science and Engineering, Bharathiar University, Coimbatore,firstname.lastname@example.org
2. Reader and Head i/c, Department of ComputerApplications
School of Computer Science and Engineering,Bharathiar University, Coimbatore,email@example.com
Acute Abdomen is defined as a syndrome induced by a wide variety of pathological conditions that require emergent medical or more often surgical management. The cardinal presenting symptom is abdominal pain which has many underlying causes. Over the past 10 years, sonography has gained acceptance for examining patients with acute abdominal pain. Sonography is dynamic, noninvasive, rapid, inexpensive, and readily accessible. It is very tedious and time consuming to analyze the sonographic images manually. The authors propose a novel method for diagnosing acute appendicitis using Euclidean distance measures. This paper details the image mining system that automates the diagnosis of acute appendicitis with significant speed up, experimentation methods, real data used for testing and the result.
Keywords: Image Mining, Euclidean Distance, Data Mining, Appendicitis, Abdomen