Ramesh R. Manza

Bio: Ramesh R. Manza is an academic researcher from Dr. Babasaheb Ambedkar Marathwada University. The author has contributed to research in topics: Feature extraction & Glaucoma. The author has an hindex of 11, co-authored 84 publications receiving 509 citations. Previous affiliations of Ramesh R. Manza include Texas A&M University.

Generalized Regression Neural Network and Radial Basis Function for Heart Disease Diagnosis

Abstract: In this paper, two types of Artificial Neural Network (ANNs), Generalized Regression Neural Network (GRNN) and Radial Basis Function (RBF) have been used for heart disease to prescribe the medicine. Diagnosing the heart disease and prescribing the medicine on the basis of symptoms is a very challenging task to improve the ability of the physicians. The training capacity and medicines provided by these two techniques are compared with the original medicines provided by the heart specialist. About 300 patients data are collected from Sahara Hospital, Aurangabad under the supervision of doctor. This study includes the detailed information about patient and preprocessing was done. The GRNN and RBF have been applied over this patient data for the outcome the medicine. The result of these evaluation show that the overall performance of RBF can be applied successfully for prescribing the medicine for the heart disease patient.

Recognition of isolated words using Zernike and MFCC features for audio visual speech recognition

Abstract: Automatic speech recognition by machine is an attractive research topic in signal processing domain and has attracted many researchers to contribute in this area. In recent year, there have been many advances in automatic speech reading system with the inclusion of audio and visual speech features to recognize words under noisy conditions. The objective of audio-visual speech recognition system is to improve recognition accuracy. In this paper we computed visual features using Zernike moments and audio feature using mel frequency cepstral coefficients on visual vocabulary of independent standard words dataset which contains collection of isolated set of city names of ten speakers. The visual features were normalized and dimension of features set was reduced by principal component analysis (PCA) in order to recognize the isolated word utterance on PCA space. The performance of recognition of isolated words based on visual only and audio only features results in 63.88 and 100 % respectively.

Detection of Osteoarthritis using Knee X-Ray Image Analyses: A Machine Vision based Approach

Abstract: Osteoarthritis is one of the popular causes of debility in elderly & overweight people. Osteoarthritis is a joint disease that invades the cartilage of bigger joints like knee, hip, feet and spine. Cartilage helps the easy glide of bones & obstructs them from rubbing each other. In Osteoarthritis cartilage is ruptured due to which bones start kneading each other with a severe pain. The scenario for the evaluation of Osteoarthritis includes clinical examination & various medical imaging techniques. In this work the authors have used Active contour segmentation technique to segment the portion/part of the knee X-ray image to diagnosis the disease. The numerous features like Haralick, Statistical, First four moments, Texture and Shape are computed and classified using Random Forest classifier. The proposed method gives the classification accuracy rate of 87.92% which are more competitive and promising with the existing algorithms.

Evaluation of partially overlapping 3d point cloud's registration by using icp variant and cloudcompare

Abstract: Terrestrial Laser Scanners (TLS) are used to get dense point samples of large object’s surface. TLS is new and efficient method to digitize large object or scene. The collected point samples come into different formats and coordinates. Different scans are required to scan large object such as heritage site. Point cloud registration is considered as important task to bring different scans into whole 3D model in one coordinate system. Point clouds can be registered by using one of the three ways or combination of them, Target based, feature extraction, point cloud based. For the present study we have gone through Point Cloud Based registration approach. We have collected partially overlapped 3D Point Cloud data of Department of Computer Science & IT (DCSIT) building located in Dr. Babasaheb Ambedkar Marathwada University, Aurangabad. To get the complete point cloud information of the building we have taken 12 scans, 4 scans for exterior and 8 scans for interior facade data collection. There are various algorithms available in literature, but Iterative Closest Point (ICP) is most dominant algorithms. The various researchers have developed variants of ICP for better registration process. The ICP point cloud registration algorithm is based on the search of pairs of nearest points in a two adjacent scans and calculates the transformation parameters between them, it provides advantage that no artificial target is required for registration process. We studied and implemented three variants Brute Force, KDTree, Partial Matching of ICP algorithm in MATLAB. The result shows that the implemented version of ICP algorithm with its variants gives better result with speed and accuracy of registration as compared with CloudCompare Open Source software.

A study of eye tracking technology and its applications

Abstract: We can measure the eye movement activity using eye tracking technology. Eye tracking gives us information about where do we look? What is ignored and how the pupil reacts to different stimuli. The eye tracking concept is basic but its process and interpretation can be very diverse and complex. ET measures the gaze points generated by our eye relative to the head. Eye trackers are availbble in either remote or mobile forms. It tracks and records where do we look and how we move the gaze. One can analyze, visualize and interpret this information with the help of software. We have gone through the common use of fingerprint analysis and applications, eye tracking also would be a great biometric tool for analysis in various applications. In this paper we discuss eye tracking technology and its various applications. Now days, ET is being employed in almost all field including psychology, human computer interaction, marketers, designers, academics, medical, research and many more.

Deep Learning in Image Classification using Residual Network (ResNet) Variants for Detection of Colorectal Cancer

Abstract: This paper investigates a deep learning method in image classification for the detection of colorectal cancer with ResNet architecture. The exceptional performance of a deep learning classification incites scholars to implement them in medical images. In this study, we trained ResNet-18 and ResNet-50 on colon glands images. The models trained to distinguish colorectal cancer into benign and malignant. We assessed our prototypes on three varieties of testing data (20%, 25%, and 40% of whole datasets). The empirical outcomes confirm that the application of ResNet-50 provides the most reliable performance for accuracy, sensitivity, and specificity value than ResNet-18 in three kinds of testing data. Upon three test assortments, we perceive the best performance value on 20% and 25% test sets with a classification accuracy of above 80%, the sensitivity of above 87%, and the specificity of above 83%. In this research, a deep learning method demonstrates the profoundly reliable and reproducible outcomes for biomedical image analysis.