SIBO PRASAD PATRO
@giet.edu
Assistant Professor
GIET UNIVERSITY
Mr. Sibo Prasad Patro received his MCA in 2012 from Sambalpur University, Odisha and his M.Tech (Computer Science and Engineering) in 2014 from BPUT, Raurkela, Odisha. He is currently pursuing his Ph.D in Computer Science and Engineering at GIET University, Gunupur, Odisha under the supervision of Dr. NeelamadhabPadhy and Dr. Rahul Deo Sah. He has published several SCI and Scopus indexing journals. He has also published few conference papers. He has more than 17 years of teaching experience. His research interest includes data mining, machine learning, deep learning, IoT and their application to engineering. Currently he is working as Assistant Professor in the department of Computer Science and Engineering, GIET University, Gunupur. He has received a best paper presentation award in ICCSEA-2020 an International Conference organized by GIET University, Gunupur.
EDUCATION
M.Tech,
RESEARCH INTERESTS
MACHINE LEARNING, IoT
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Sibo Prasad Patro and Neelamadhab Padhy
Springer Nature Singapore
Sibo Prasad Patro and Neelamadhab Padhy
MDPI
Sibo Prasad Patro and Neelamadhab Padhy
MDPI
Rahul Deo Sah, Neelamadhab Padhy, Nagesh Salimath, Sibo Prasad Patro, Syed Jaffar Abbas, and Raja Ram Dutta
Springer Nature Singapore
Sibo Prasad Patro, Neelamadhab Padhy, and Rahul Deo Sah
Springer Nature Singapore
Sibo Prasad Patro, Neelamadhab Padhy, and Rahul Deo Sah
IGI Global
There are very few studies are carried for investigating the potential of hybrid ensemble machine learning techniques for building a model for the detection and prediction of heart disease in the human body. In this research, the authors deal with a classification problem that is a hybridization of fusion-based ensemble model with machine learning approaches, which produces a more trustworthy ensemble than the original ensemble model and outperforms previous heart disease prediction models. The proposed model is evaluated on the Cleveland heart disease dataset using six boosting techniques named XGBoost, AdaBoost, Gradient Boosting, LightGBM, CatBoost, and Histogram-Based Gradient Boosting. Hybridization produces superior results under consideration of classification algorithms. The remarkable accuracies of 96.51% for training and 93.37% for testing have been achieved by the Meta-XGBoost classifier.
Sibo Prasad Patro and Neelamadhab Padhy
IGI Global
Cardiovascular disease is one of the deadliest diseases in the world. Accurate analysis and prediction for real-time heart disease are highly significant. To address this challenge, a novel IoT-based automated function monitoring system to promote the e-healthcare system is proposed. The proposed remote healthcare monitoring system uses an IoT framework (RHMIoT) using deep learning and auto encoder-based machine learning algorithms to accurately predict the presence of heart disease. The RHMIoT framework contains two phases: the first phase is to monitor the severity level of the heart disease patient in real-time, and the second phase is used in the medical decision support system to predict the accuracy level of heart disease. To train and test the open-access Framingham dataset, various deep learning and auto encoder-based machine learning techniques are used. The proposed system obtains an accuracy of 0.8714% using the auto encoder-based kernel SVM algorithm compared to other approaches.
Sibo Prasad Patro, Neelamadhab Padhy, and Rahul Deo Sah
IEEE
Accurate analysis and prediction for real-time heart disease are highly significant. Many medical diagnosis difficulties have a class imbalance because the number of patients with a certain disease is significantly smaller than the number of healthy people in the population. The purpose of this work is to provide a way for using a feature selection technique to determine the most relevant features of heart disease characteristics. The experiment for this study is performed over the Framingham Heart Study dataset using OneR, GA, and CORR feature selection methods. With the help of the Chi-squared test, six highly correlated features are selected for disease prediction. The experimental results show that CORR has the lowest mean rank of 8.16% and the accuracy for the proposed model using SVM outperformed with an accuracy of 67% on oversampling data.
Sibo Prasad Patro, Neelamadhab Padhy, and Rahul Deo Sah
Springer Nature Switzerland
Sibo Prasad Patro, Neelamadhab Padhy, and Rahul Deo Sah
Inderscience Publishers
Sibo Prasad Patro, Neelamadhab Padhy, and Dukuru Chiranjevi
Springer Science and Business Media LLC
Rahul Deo Sah, Sibo Prasad Patro, Neelamadhab Padhy and Nagesh Salimath
Data mining plays an important role in disease symptoms prediction. A number of diseases like prediction of heart disease, breast cancer prediction,diabetics patients analysis using data maning techniques are involved. Diabetes and their symptoms are well verse-known, as the spreading of information technology and their continued involvement in the medical and health fields. Its help to find solutions for diagnosis the dieases and treatment. Using data models to classify the dataset for predition of disease. The classification technique is to have quicker and more diverse solutions. Two algorithmic trends are Deep learning and another one Gradient Boosted Trees to achieve the predicted value 32.20 and 27.73. The Deep Learning performance is better then Gradient Boosted Trees which is appearance in the research.
Sibo Prasad Patro, Gouri Sankar Nayak, and Neelamadhab Padhy
Elsevier BV
Sibo Prasad Patro, Padmaja Patel, Murali Krishna Senapaty, Neelamadhab Padhy, and Rahul Deo Sah
IEEE
Today, due to the growth of IoT(Internet of Things) the concept of smart cities has gained considerable popularity. To maximize the productivity and reliability of urban infrastructure consistent efforts are being made in the field of IoT. Many problems such as traffic congestion and road safety are being solved by the use of IoT. Today peoples face a common problem in the parking area to find a free parking slot in cities. In this study, we are designing a Smart Parking System, which will enable the user to find Parking slots in a given parking area. It also avoids unnecessary traveling through filled parking lots. In this paper, the author presents a smart parking system with the help of IoT over Wi-Fi. This intelligent parking system consists of an IoT module that helps to track the availability of each single vacant parking space. The author used an Arduino Uno, which can be embedded over the Wi-Fi module to establish a connection to the internet. This technology helps to transfer the data live. In this smart parking system, with the help of digital IR sensors, the system gets the status regarding the parking slot status, whether it is occupied or vacant. This sensor sends the collected data to the microcontroller. Latter the data are processed, and the status of parking slots is updated in the central database. The IR sensors need to be deployed in the appropriate locations so that the system can cover all the parking slots. Each parking slot is identified with a unique id to identify them on the network
Neelamadhab Padhy, Abhinandan Panda, and Sibo Prasad Patro
Springer Singapore