SIBO PRASAD PATRO

@giet.edu

Assistant Professor
GIET UNIVERSITY



              

https://researchid.co/sibofromgiet

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

16

Scopus Publications

231

Scholar Citations

6

Scholar h-index

4

Scholar i10-index

Scopus Publications




  • Comparative Analysis Using Data Mining Techniques to Predict the Air Quality and Their Impact on Environment
    Rahul Deo Sah, Neelamadhab Padhy, Nagesh Salimath, Sibo Prasad Patro, Syed Jaffar Abbas, and Raja Ram Dutta
    Springer Nature Singapore

  • A Road Map for Classification of Heart Disease Using Machine Learning Classifier
    Sibo Prasad Patro, Neelamadhab Padhy, and Rahul Deo Sah
    Springer Nature Singapore

  • Heart rate monitoring using IoT and AI for aged person: A survey


  • An Ensemble Approach for Prediction of Cardiovascular Disease Using Meta Classifier Boosting Algorithms
    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.

  • An RHMIoT Framework for Cardiovascular Disease Prediction and Severity Level Using Machine Learning and Deep Learning Algorithms
    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.

  • Classification model for heart disease prediction using correlation and feature selection techniques
    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.

  • Anticipation of Heart Disease Using Improved Optimization Techniques
    Sibo Prasad Patro, Neelamadhab Padhy, and Rahul Deo Sah
    Springer Nature Switzerland

  • An improved ensemble learning approach for the prediction of cardiovascular disease using majority voting prediction
    Sibo Prasad Patro, Neelamadhab Padhy, and Rahul Deo Sah
    Inderscience Publishers

  • Ambient assisted living predictive model for cardiovascular disease prediction using supervised learning
    Sibo Prasad Patro, Neelamadhab Padhy, and Dukuru Chiranjevi
    Springer Science and Business Media LLC

  • Diabetics patients analysis using deep learning and gradient boosted trees
    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.

  • Heart disease prediction by using novel optimization algorithm: A supervised learning prospective
    Sibo Prasad Patro, Gouri Sankar Nayak, and Neelamadhab Padhy
    Elsevier BV

  • IoT based Smart Parking System: A Proposed Algorithm and Model
    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

  • A Cyclic Scheduling for Load Balancing on Linux in Multi-core Architecture
    Neelamadhab Padhy, Abhinandan Panda, and Sibo Prasad Patro
    Springer Singapore

RECENT SCHOLAR PUBLICATIONS

  • Analysis of Cardiovascular Disease Prediction Using Various Machine Learning and Deep Learning Algorithms
    SP Patro, N Padhy
    Intelligent Technologies: Concepts, Applications, and Future Directions 2024

  • Integration of IoT and Machine Learning for Real-Time Monitoring and Control of Heart Disease Patients
    N Padhy, R Panigrahi, SP Patro, VK Swain, KK Sahu
    Proceedings 105 (1), 32 2024

  • A Secure IoT-Cloud Based Remote Health Monitoring for Heart Disease Prediction Using Machine Learning and Deep Learning Techniques
    SP Patro, N Padhy
    Engineering Proceedings 56 (1), 241 2023

  • A secure remote health monitoring for heart disease prediction using machine learning and deep learning techniques in explainable artificial intelligence framework
    SP Patro, N Padhy
    Engineering Proceedings 58 (1), 78 2023

  • A Secure Remote Health Monitoring for Heart Disease Prediction using machine learning and deep learning techniques in XAI framework
    SP PATRO, N Padhy
    Chem. Proc 56 2023

  • Comparative Analysis Using Data Mining Techniques to Predict the Air Quality and Their Impact on Environment
    RD Sah, N Padhy, N Salimath, SP Patro, SJ Abbas, RR Dutta
    Proceedings of Fourth International Conference on Computer and Communication 2023

  • Classification model for heart disease prediction using correlation and feature selection techniques
    SP Patro, N Padhy, RD Sah
    2022 OITS International Conference on Information Technology (OCIT), 29-34 2022

  • Anticipation of heart disease using improved optimization techniques
    SP Patro, N Padhy, RD Sah
    International Conference on Computing, Communication and Learning, 91-102 2022

  • A Road Map for Classification of Heart Disease Using Machine Learning Classifier
    SP Patro, N Padhy, RD Sah
    Next Generation of Internet of Things: Proceedings of ICNGIoT 2022, 687-702 2022

  • Heart Rate Monitoring Using IoT and AI for Aged Person: A Survey
    SP Patro, N Padhy, RD Sah
    The Role of IoT and Blockchain, 39-59 2022

  • An ensemble approach for prediction of cardiovascular disease using meta classifier boosting algorithms
    SP Patro, N Padhy, RD Sah
    International Journal of Data Warehousing and Mining (IJDWM) 18 (1), 1-29 2022

  • An improved ensemble learning approach for the prediction of cardiovascular disease using majority voting prediction
    SP Patro, N Padhy, RD Sah
    International Journal of Modelling, Identification and Control 41 (1-2), 68-86 2022

  • An RHMIoT Framework for cardiovascular disease prediction and severity level using machine learning and deep learning algorithms
    SP Patro, N Padhy
    International Journal of Ambient Computing and Intelligence (IJACI) 13 (1), 1-37 2022

  • Ambient assisted living predictive model for cardiovascular disease prediction using supervised learning
    SP Patro, N Padhy, D Chiranjevi
    Evolutionary intelligence 14 (2), 941-969 2021

  • Diabetics Patients Analysis Using Deep Learning and Gradient Boosted Trees
    RD Sah, SP Patro, N Padhy, N Salimath
    2021 8th International Conference on Computing for Sustainable Global 2021

  • Heart disease prediction by using novel optimization algorithm: A supervised learning prospective
    SP Patro, GS Nayak, N Padhy
    Informatics in Medicine Unlocked 26, 100696 2021

  • IoT based smart parking system: a proposed algorithm and model
    SP Patro, P Patel, MK Senapaty, N Padhy, RD Sah
    2020 International Conference on Computer Science, Engineering and 2020

  • A Cyclic Scheduling for Load Balancing on Linux in Multi-core Architecture
    N Padhy, A Panda, SP Patro
    Smart Intelligent Computing and Applications: Proceedings of the Third 2020

  • A Cyclic Scheduling for Load Balancing on Linux in Multi-core Architecture
    DNP Sibo Prasad Patro
    Smart Innovation, Systems and Technologies book series (SIST) 160 2019

  • Analysis of Decision Tree Construction: A Data Mining Approach
    SKR Sibo Prasad Patro
    International Journal of Research and Analytical Reviews 6 (special issue), 4 2019

MOST CITED SCHOLAR PUBLICATIONS

  • Heart disease prediction by using novel optimization algorithm: A supervised learning prospective
    SP Patro, GS Nayak, N Padhy
    Informatics in Medicine Unlocked 26, 100696 2021
    Citations: 96

  • Ambient assisted living predictive model for cardiovascular disease prediction using supervised learning
    SP Patro, N Padhy, D Chiranjevi
    Evolutionary intelligence 14 (2), 941-969 2021
    Citations: 58

  • Security issues over E-commerce and their solutions
    SP Patro, N Padhy, R Panigrahi
    Int. J. of Advanced Research in Computer and Communication Engineering 5 (12) 2016
    Citations: 20

  • IoT based smart parking system: a proposed algorithm and model
    SP Patro, P Patel, MK Senapaty, N Padhy, RD Sah
    2020 International Conference on Computer Science, Engineering and 2020
    Citations: 19

  • An improved ensemble learning approach for the prediction of cardiovascular disease using majority voting prediction
    SP Patro, N Padhy, RD Sah
    International Journal of Modelling, Identification and Control 41 (1-2), 68-86 2022
    Citations: 9

  • A secure remote health monitoring for heart disease prediction using machine learning and deep learning techniques in explainable artificial intelligence framework
    SP Patro, N Padhy
    Engineering Proceedings 58 (1), 78 2023
    Citations: 6

  • An RHMIoT Framework for cardiovascular disease prediction and severity level using machine learning and deep learning algorithms
    SP Patro, N Padhy
    International Journal of Ambient Computing and Intelligence (IJACI) 13 (1), 1-37 2022
    Citations: 4

  • Diabetics Patients Analysis Using Deep Learning and Gradient Boosted Trees
    RD Sah, SP Patro, N Padhy, N Salimath
    2021 8th International Conference on Computing for Sustainable Global 2021
    Citations: 4

  • An ensemble approach for prediction of cardiovascular disease using meta classifier boosting algorithms
    SP Patro, N Padhy, RD Sah
    International Journal of Data Warehousing and Mining (IJDWM) 18 (1), 1-29 2022
    Citations: 3

  • A Cyclic Scheduling for Load Balancing on Linux in Multi-core Architecture
    N Padhy, A Panda, SP Patro
    Smart Intelligent Computing and Applications: Proceedings of the Third 2020
    Citations: 3

  • A Secure IoT-Cloud Based Remote Health Monitoring for Heart Disease Prediction Using Machine Learning and Deep Learning Techniques
    SP Patro, N Padhy
    Engineering Proceedings 56 (1), 241 2023
    Citations: 2

  • Anticipation of heart disease using improved optimization techniques
    SP Patro, N Padhy, RD Sah
    International Conference on Computing, Communication and Learning, 91-102 2022
    Citations: 2

  • Analysis of Information Security through Crypto - Stenography with Reference to E - Cipher Methods
    SP Patro
    International Journal of Advanced Research in Computer and Communication 2017
    Citations: 2

  • Classification model for heart disease prediction using correlation and feature selection techniques
    SP Patro, N Padhy, RD Sah
    2022 OITS International Conference on Information Technology (OCIT), 29-34 2022
    Citations: 1

  • A Road Map for Classification of Heart Disease Using Machine Learning Classifier
    SP Patro, N Padhy, RD Sah
    Next Generation of Internet of Things: Proceedings of ICNGIoT 2022, 687-702 2022
    Citations: 1

  • Heart Rate Monitoring Using IoT and AI for Aged Person: A Survey
    SP Patro, N Padhy, RD Sah
    The Role of IoT and Blockchain, 39-59 2022
    Citations: 1