Papers by Mr. Radhanath Patra
Machine Learning Classification Algorithms for the Prediction of Diseases
River Publishers eBooks, Jun 20, 2024
Prediction of COVID-19 X-Ray Image Using DenseNet Transfer Learning
Hybrid Optimization-Based Structural Design of Deep Q Network With Feature Selection Algorithm for Medical Data Classification
International Journal of Swarm Intelligence Research
In the area of medical informatics, the medical data classification is considered a complicated j... more In the area of medical informatics, the medical data classification is considered a complicated job. However, accurate classification of medical data is a complex task. Therefore, a robust and effective hybrid optimization-based deep learning method for classifying the medical data is developed in this research. The input data is pre-processed using data normalization method. Then, the features are selected using the proposed Henry Sea Lion Optimization (HSLnO), which is the combination of Henry Gas Solubility Optimization (HGSO) and Sea Lion Optimization (SLnO). The classification process is achieved using an optimized Deep Q Network (DQN). The DQN is optimized using the proposed Shuffled Shepherd Whale optimization Algorithm (SSWOA). The proposed SSWOA is developed by the integration of the Shuffled Shepherd Optimization Algorithm (SSOA) and Whale Optimization Algorithm (WOA). The developed technique achieves better performance of testing accuracy, sensitivity, and specificity wit...
Fractional rider gradient descent applied U-Net based segmentation with optimal deep maxout network for lung cancer classification using histopathological images
Research on Biomedical Engineering, 2022
Analysis and Prediction Of Pima Indian Diabetes Dataset Using SDKNN Classifier Technique
IOP Conference Series: Materials Science and Engineering, 2021
The newly proposed weighted k nearest neighbour is known as standard deviation K nearest neighbou... more The newly proposed weighted k nearest neighbour is known as standard deviation K nearest neighbour(SDKNN) classifier technique. It is based on the principle of standard deviation. Standard deviation measures spreading of attribute about mean. Spreading of attribute plays a significant role to improve the classification accuracy of a dataset. Most of our distance calculation method between two points is determined by using euclidean distance process for finding nearest neighbour. Our proposed technique is based on a new distance calculation formula to find nearest neighbour in KNN. We apply here standard deviations of attributes as power for calculating distance between train dataset and test dataset. Distance calculation between two points in k nearest neighbour classifier is modified according to the standard deviation of attribute. In this paper, standard deviation of attributes are used. In first attempt, we have used standard deviation of attributes as power for calculating K Ne...
Predictive Analysis of Rapid Spread of Heart Disease with Data Mining
2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), 2019
cardiovascular disease is the leading cause of mortality for both sexes in worldwide. Heart disea... more cardiovascular disease is the leading cause of mortality for both sexes in worldwide. Heart disease is increasing at a rapid rate in both older and younger generation of males and females now days. So in need demand of right strategies, development and implementation of effective health monitoring policies should be emphasized to combat the epidemic of heart related diseases. So early detection and treatment with the use of both conventional and innovative technique must be preferred In this paper we have used the UCI machine learning repository Cleveland heart disease database having 303 instance and 76 attributes. For the proposed method we have used the Information gain concept for selection of best attribute and processes the selected features using weka and python. This paper identifies the gap of research on prediction of heart disease based on python Anaconda navigator, spyder and weka platform on which we have much emphasized. The various techniques, processes which have used to train the model of heart datasets such as feature selection, numpy, pandas library, decision tree classifier, KNN classifier, entropy, gini-index, confusion matrix.The result shows that decision tree classifier is most effective and appropriate for prediction of UCI repository Cleveland heart dataset.
Communications in Computer and Information Science, 2020
Introduction: Sodium-glucose co-transporter 2 inhibitors (SGLT2is) are a unique class of drugs cu... more Introduction: Sodium-glucose co-transporter 2 inhibitors (SGLT2is) are a unique class of drugs currently used in the management of type 2 diabetes (T2D). There are emerging data from cardiovascular outcome trials confirming renal and heart failure benefits of these drugs independent of glucose lowering. By contrast, the current licencing indications of these drugs are mainly limited to their glucose-lowering effects, and not to renal or heart failure benefits. It is therefore timely to ascertain whether the presence of these clinical conditions may influence prescribing choices for patients with T2D. Our aims are to report prescribing of SGLT2is in people with T2D according to their renal function and presence of heart failure. Co-prescribing with diuretics will also be explored. Methods: We will perform a cross-sectional analysis of people with T2D in the Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) network. The RCGP RSC includes more than 1500 volunteer practices throughout England and parts of Wales, and a representative sample of over 10 million patients. The proportion of adults with T2D ever prescribed an SGLT2i will be determined. Within this cohort, we will calculate the percentage of SGLT2is prescribed according to renal function, and the proportion of prescriptions in people with co-morbid heart failure, stratified by body mass index categories. The percentage of SGLT2is prescribed as an add-on to a diuretic or following discontinuation of prescribing for a diuretic will also be reported. Multilevel logistic regression will be performed to explore the association between heart failure and renal function, and propensity to prescribe SGLT2is. Planned Outputs: The study findings will be submitted to a primary care/diabetes-focused conference, and for publication in a peer reviewed journal.
International Journal of Smart Sensor and Adhoc Network., 2013
The demand towards broad band efficient antennas for base station and mobile wireless application... more The demand towards broad band efficient antennas for base station and mobile wireless applications have increased dramatically over the last few years. Today there is a huge increase in the number of subscribers and demand for equipments that is capable of handling cost-effective network capacity solutions in Spectrum limited markets. Our Paper describes the design of dual polarized antenna element which can be implemented in a base station antenna array using IE3D Zeland Software. The Element is based on aperture coupled architecture with stacked patch, maintaining the symmetry needed for dual polarization operation. Most of common antenna elements are linearly polarized with narrow band resonators. Our design has Broad-band and dual-polarized characteristics of traditional aperture coupled architecture. The Bandwidth for Return Loss > 10 dB of the element covers 1710-2170 MHz frequency spectrum. The Isolation between the ports corresponding to the two different polarizations is...
International Journal of Advanced Research, 2016
An Adaptive method for Image Deblurring is presented here. Processing of image data collected fro... more An Adaptive method for Image Deblurring is presented here. Processing of image data collected from both surveillance camera and on road traffic control motor vehicle camera is a big issue because often the objects are in motion and sometimes both the objects and camera are not steady. This leads to Blurring of the image and further image processing is not possible due to the degradation of received image. So Image Deblurring techniques are applied before enhancement or further processing. But it needs proper data for Deblurring like the frequency characteristics (Point Spread Function (PSF)) and Noise characteristics (Noise-to-Signal Power Ratio(NSR)). The method presented here gives the above information along with the motion information. The information about motion detection is very important because in the Deblurring process the noise estimation cannot be done without knowing actual pixels of the sensor noise present in the image. So to get a deblurred image with proper noise reduction that can be further processed in the RTS (Road Traffic & Safety) controller required information are provided sequentially according to the motion detection and Deblurring algorithm. This method uses some good Deblurring methods like Blind Deconvolution and Regularization filtering along with proper motion detections and characteristics estimations to get an image close to the true image which is sufficient for further processing.
IOSR Journal of Electronics and Communication Engineering, 2013
In this paper, a simple and dynamic wavelet-based algorithm is presented for enhancement of the i... more In this paper, a simple and dynamic wavelet-based algorithm is presented for enhancement of the image sharpness or blurriness of an Image. Four set of methods are followed here (Denoising, Decomposition, Sharpness Estimation, and Filtering). First Denoising is done on the input images and then it operates by initially decomposing the input image through a multi-level separable DWT. After this, the log-energies of the DWT sub bands are computed. A Scalar Index corresponding to the input image's sharpness is computed through the weighted average of the computed log-energies. Several Satellite images are taken into consideration and the Scalar Sharpness Index representing the image's overall sharpness denoted as SSI. This is used as a filtering component and the image is filtered out to give the Sharpened Image. Here along with the Scalar Sharpness Index, a Block based algorithm is presented to determine the local perceived sharpness. The Block Based Scalar Sharpness Index is calculated by taking the RMS of 0.01 of largest value of the filtering parameter which takes the no. of Block Size. This proposed method is the simplest, fastest and accurate comparing to the currently best-performing techniques for the sharpness estimation.
Improvement of Clustering Classification Performance Using Row-Based Filtering for health care application
A combination of unsupervised and supervised machine learning with an outlier-based row filtering... more A combination of unsupervised and supervised machine learning with an outlier-based row filtering technique is proposed in this paper. Our idea is based upon the concept of row-based filtering technique, a novel approach of records selection for a given structured dataset. Initially, dropping the class label, dataset is divided into groups by using clustering algorithm and class labels are assigned to the clusters. The cluster labels are compared with the original class labels. Records having matching class label and cluster label are extracted to frame a dataset having matching record. Remaining records after filtering matching records are treated as outliers. UCI repository datasets such as Breast cancer, Pima Indian diabetes dataset, Hungarian dataset, Indian liver patient dataset and Cleveland heart dataset are used to analyze the performance of proposed method. For clustering K means clustering algorithm is used and the classification performance is measured by applying various...
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Papers by Mr. Radhanath Patra