Shekhar Rambhau Suralkar

@sscoetjalgaon.ac.in

Professor, Computer Engineering Department
SSBT's College of Engineering and Technology Bambhori Jalgaon

Shekhar Rambhau Suralkar


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https://researchid.co/shekharsuralkar

RESEARCH, TEACHING, or OTHER INTERESTS

Electrical and Electronic Engineering
12

Scopus Publications

Scopus Publications

  • Machine learning approach for cardiovascular disease prediction
    Prashant Maganlal Goad, Pramod J. Deore, S. R. Suralkar
    Integrated Technologies in Electrical Electronics and Biotechnology Engineering, 2025
    Information classification is one of the many widely used machine learning applications. Machine learning enables the extraction of features from large databases and datasets used in commercial operations. In the healthcare sector, artificial intelligence is becoming a hot issue for research, mainly due to its ability to make predictions and provide a more thorough comprehension of clinical data. The majority of machine learning techniques rely on specific characteristics that determine the methods behavior, impact their output, and specify the amount of information included in the models that are produced. Numerous methods based on machine learning are currently employed to diagnose cardiac conditions. Neural network analysis and logistic regression are two of the few often used machine learning approaches in the field of diagnosing cardiac disease. They look at a range of approaches, such as neural networks, closest neighbors, logistic regression, naïve bayes including composite strategies that incorporate the previously mentioned coronary artery identification algorithms. The Python computing framework was utilized to create and train the algorithm utilizing the UCI machine learning repository standard set. The project’s framework can be enlarged to allow for the collection of more data. The structure of the project can be expanded to accommodate extra information collecting.
  • Performance Evaluation of Light Weight Cryptographic CLEFIA Algorithm
    Atul H. Karode, Shekhar R. Suralkar, Vaishali B. Patil
    Ssrg International Journal of Electronics and Communication Engineering, 2023
    We have become more cognizant of communication since the 19th century. However, we have known how important communication is for daily life for the last three or four decades. It is merely a method of transmitting data from one end to the other. A transmitter and a receiver are the two ends. Both of these components must be present for communication to succeed. As the application of this process continues to grow, new techniques or tactics have been developed. The human being then learned that communication is crucial, but so is keeping that communication safe. Claimed to be a trustworthy cipher is CLEFIA. The CLEFIA specs and algorithm design can be assessed for performance and security by cryptographers and the general public. For ISO/IEC lightweight cryptography, the CLEFIA cipher is a popular choice. It is a generalised Feistel network with four nodes. This architecture requires little room, both physically and virtually. The Diffusion Switching Mechanism in CLEFIA shields the system from serious assaults. Additionally, the primary scheduling and data processing components of CLEFIA perform comparable tasks, which decreases the gate size. In this study, the execution times of the Low Weight Cryptographic CLEFIA old and modified algorithms are discussed. This calls for using a product from Texas Instruments (TI), the MSP-EXP430FR5994 LaunchPad Development Kit from the MSP430 family. The CLEFIA supports 128- bit blocks and offers 192-bit and 256-bit key sizes.
  • Prediction of Epilepsy Seizures by Machine Learning Methods
    Mayuri Tushar Deshmukh, Shekhar R. Suralkar
    International Journal on Recent and Innovation Trends in Computing and Communication, 2023
    According to the Globe Health Organization (WHO), more than 50 million people throughout the world are living with a diagnosis of epilepsy, making it perhaps of the most widely recognized neurological issue. Epileptic seizures are a leading cause of hospitalization and mortality across the globe. Accurate and prompt diagnosis is more crucial than ever given the increase in epileptic seizures all through the globe and their effect on individuals' lives. Epilepsy, cancer, diabetes, heart disease, thyroid disease, and many more are only some of the diseases for which machine learning approaches are being applied in prediction and diagnosis. Epilepsy is one ailment that may be treated early on to save a person's life. The main objective of this research is to use feature label extraction to the dataset in order to obtain the best ML models for epileptic seizures. In order to predict epilepsy, we used the techniques of logistic regression, SVM, linear SVM, KNN, and RNN in this study. The models employed in this research are accurate to varying degrees and have attributes including precision, recall, f1-score, and support. This study demonstrates that the model is able to accurately predict the occurrence of epilepsy. Our discoveries demonstrate that involving Examination highlight extraction in the dataset, the Regional Neural Network (RNN) model with 99.9998 % Training data accuracy and 97.78% Test data accuracy and 100% prediction probability of epilepsy seizure produces the best results and also the feature characteristics of RNN is better as compared to other models used in current research work.
  • Machine Learning Classifiers to Detecting Epileptic Seizures
    Mayuri Tushar Deshmukh, Shekhar R. Suralkar
    2023 IEEE International Conference on Integrated Circuits and Communication Systems Icicacs 2023, 2023
    Examining the neuronal signals that brain neurons produce may help detect the serious chronic neurological disease known as epilepsy. Neurons are tightly connected to one another in order to transmit signals and communicate with internal organs. To identify these brain impulses, electroencephalogram (EEG) in addition electrocorticography (ECoG) media are usually employed. The signals are intricate, noisy, not stationary, and not linear, also they a plenty of data to create. As a consequence, finding knowledge about the brain and identifying seizures are tough tasks. Classifiers for machine learning can categorize EEG data, identify seizures, also showcase significant patterns without losing performance. As a consequence, several academics have developed a range of seizure detection techniques that make use of statistical traits then machine learning classifiers. The selection of appropriate classifiers and attributes presents the most challenges. This paper's goal is to provide a summary for the many different variations of these approaches over the past several years depending on the classification of statistical characteristics also “black-box” also “non-black-box” classifiers for machine learning. Cutting-edge techniques and concepts discussed will provide a meticulous clutch of seizure detection, enumeration, and future research prospects.
  • Performance Monitoring of High Voltage Power Supply using Flyback Converter with Extra High Tension
    13th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2022, 2022
  • Deep Learning for Crowd Image Classification for Images Captured Under Varying Climatic and Lighting Condition
    Hemant T. Ingale, Shekhar S. Suralkar, Anil J. Patil
    Ibssc 2022 IEEE Bombay Section Signature Conference, 2022
    Most of recent events have attracted a lot of attention towards importance of automatic crowd classification and management. COVID-19 is the most setback for the entire world. During these events proper breakout and public crowd management leads to the requirement of managing, counting, securing as well as tracking the crowd. But automatic analysis of the crowd is very challenging task because of varying climatic and lighting conditions, varying postures etc. During this paper we have developed PYTHON based system for automatic crowd images classification using Deep learning. This paper is the first attempt for automatic classification of crowd images. We have prepared the dataset of crowd classification consisting of three categories. The proposed methodology of crowd classification starts with preprocessing during which we have used median filtering for noise removal. Deep learning models are developed using 70% training images. The performance of the system is evaluated for various deep learning algorithms including one block VGG, two block VGG and three block VGG. We have also evaluated the performance of three block VGG using dropout. VGG16 transfer learning based crowd classification is developed using PYTHON. Using VGG16 transfer learning we achieved the accuracy of 69.44.% which is highest among all deep learning classification models during this study
  • Evaluating the Performance of POX and RYU SDN Controllers Using Mininet
    Nafees M. Kazi, Shekhar R. Suralkar, Umesh S. Bhadade
    Communications in Computer and Information Science, 2021
  • Image encryption based on matrix factorization
    Vivek Khalane, Shekhar Suralkar, Umesh Bhadade
    International Journal of Safety and Security Engineering, 2020
    In this paper, we present a matrix decomposition-based approach for image cryptography. The proposed method consists of decomposing the image into different component and scrambling the components to form the image encryption technique. We use two different type of matrix decomposition techniques to check the efficiency of proposed encryption method. The decomposition techniques used are Independent component analysis (ICA) and Non-Negative Matrix factorization (NMF). The proposed technique has unique user defined parameters (key) such as decomposition method, number of decomposition components and order in which the components are arranged. The unique encryption technique is designed on the basis of these key parameters. The original image can be reconstructed at the decryption end only if the selected parameters are known to the user. The design examples for both decomposition approaches are presented for illustration purpose. We analyze the complexity and encryption time of cryptography system. Results prove that the proposed scheme is more secure as it has less correlation between the input image and the encrypted version of the same as compared to state-of-art methods. The computation time of the proposed approach is found to be comparable.
  • Implementation of real time digital watermarking system for video authentication using FPGA
    Ashish S. Bhaisare, A. H. Karode, S. R. Suralkar
    Proceedings International Conference on Global Trends in Signal Processing Information Computing and Communication Icgtspicc 2016, 2017
    This paper presents a hardware implementation of a digital watermarking system that can insert invisible, semi fragile watermark information into compressed video streams in real time. The watermark embedding is processed in the discrete cosine transform domain. Hardware implementation using field programmable gate array has been done, and project was carried out using a custom versatile breadboard for overall performance evaluation which become a viable target for the implementation of real time algorithms suited to video image processing applications. This System is developed on Xilinx Spartan3 Field Programmable Gate Array (FPGA) device using embedded development kit (EDK) tools from Xilinx. The results showed that the proposed algorithm has a very good hidden invisibility, good security and robustness for a lot of hidden attacks.
  • A compact hexagonal shaped patch antenna for UWB applications using CPW feed
    Y.S. Santawani, S.R. Suralkar
    2015 International Conference on Pervasive Computing Advance Communication Technology and Application for Society Icpc 2015, 2015
    In this paper, the hexagonal shaped patch antenna feed by Co-Planar Waveguide (CPW) has been designed and analyzed using method of moments based electromagnetic simulator IE3D.The antenna is fabricated using a substrate having a dielectric constant of 4.4 (relative permittivity=4.4) and thickness of 1.6mm. The simulated results such as return loss, efficiency, Voltage Standing Wave Ratio (VSWR)and Gain are investigated. The antenna having a return loss less than -10dB from 2.9GHz to 12GHz which is applicable to Ultra Wide Band (UWB) Application. The point of attraction of this antenna is the use of single patch which makes it easy to fabricate.
  • Rotation invariant thinning algorithm to detect ridge bifurcations for fingerprint identification
    P.M. Patil, S.R. Suralkar, F.B. Sheikh
    Proceedings International Conference on Tools with Artificial Intelligence Ictai, 2005
  • Fingerprint verification based on fixed length square finger code
    P.M. Patil, S.R. Suralkar, H.K. Abhyankar
    Proceedings International Conference on Tools with Artificial Intelligence Ictai, 2005