A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Ramalingam, V.
- Effect of Sodium Fluoride on Serum Testosterone and Lipid Profiles in the Testis of Adult Albino Rats
Authors
1 Department of Zoology, Bharathidasan Govt. College for Women, Pondicherry-605 003, IN
2 Department of Zoology, KM Centre for Post Graduate Studies, Pondicherry-605 008, IN
Source
Journal of Endocrinology and Reproduction, Vol 7, No 1&2 (2003), Pagination: 73-73Abstract
Effect of sodium fluoride at two different doses (10 mg and 20 mg/kg body weight, orally daily for 30 days) was seen on serum testosterone and lipid profiles in the testis of adult albino rats. Testosterone was significantly decreased in 20 mg treated animals and insignificantly decreased in 10 mg treated animals. All the lipid classes were markedly decreased in the testis of fluoride treated animals.- Cognitive Intelligent Tutoring System based on Affective State
Authors
1 Department of Computer Science and Engineering, Annamalai University, Chidambaram - 608002, Tamil Nadu, IN
2 Department of Computer Science and Engineering, Annamalai University, Chidambaram - 608002, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 8, No 24 (2015), Pagination:Abstract
This paper presents a novel Intelligent Tutoring System (ITS). The goal of our virtual tutor is to mimic like a human tutor in order to advance the communication and effectiveness of the students learning experience. Towards these goals we designed a Conversational Intelligent Tutoring System (CITS) with multimodal behavior, emotive speech and friendliness. Moreover to improve the student’s interaction and to engage their attention, our virtual tutor attempts to ask questions from their thought lesson and assists the students during learning activities. A statistical Friedman analysis was conducted and the results revealed that, our intelligent virtual tutoring system can able to successfully recognize student’s behavior and respond according to it. And also participants were asked later to rate the teaching and learning environment of our intelligent system, the review shows that they feel lively.Keywords
Emotive Speech, Friendliness Behavior, Virtual Agent- Tumor Diagnosis in MRI Brain Image using ACM Segmentation and ANN-LM Classification Techniques
Authors
1 Department of Computer Science and Engineering, Annamalai University, Chidambaram - 608002, Tamil Nadu, IN
2 Department of Computer Science and Engineering, P. S. R. Rengasamy College of Engineering for Women, Sivakasi - 626140, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 9, No 1 (2016), Pagination:Abstract
Background: Magnetic Resonance Images (MRI) is an important medical diagnosis tool for the detection of tumours in brain as it provides the detailed information associated to the anatomical structures of the brain. MRI images help the radiologist to find the presence of abnormal cell growths or tumours. MRI image analysis plays a vital role in diagnosis of brain tumours in the earlier stages and treatment of diseases. Methods: Therefore, this paper introduces an efficient MRI brain image analysis method, where, the MRI brain images are classified into normal, non cancerous (benign) brain tumour and cancerous (malignant) brain tumour. This proposed method follows four steps, 1. Pre-processing, 2. Segmentation, 3. Textural and shape feature extraction and 4. Classification. In this proposed MRI image analysis using the region based Active Contour Method (ACM) used for segmentation and Artificial Neural Network (ANN) based Levenberg-Marquardt (LM) algorithm used for classification process, which used to efficiently classify the MRI image as normal and Tumourous. Findings: The results revealed that the proposed MRI brain image tumour diagnosis process is accurate, fast and robust. The classifier based MRI brain image processing approach produced the best MRI brain image classification with use of feature extraction and segmentation results, in terms of accuracy. Best overall classification accuracy results were obtained using the given DioCom Images; The performance results proven that there is not sufficient result given to the classification process when it perform separately. With the use of ACM segmentation and feature extraction approaches, the proposed LM classification approach provides better classification accuracy than the existing approach. Application: The proposed MRI image based brain tumour analysis would efficiently deal with segmentation and classification process for brain tumour analysis with use of feature extraction methods, so this method can yield the better result of brain tumour diagnosis in advance where this method using in medical fields.