Dr Mrinal Bachute

Scopus Publications

Automatic Diverse Epileptic Seizure Detection Model Using SWA-LeNet
Milind Ashok Natu, Mrinal Rahul Bachute
Circuits Systems and Signal Processing, 2026
Optimized dynamic global structure enhanced multi-channel graph neural network based automatic cataract disease classification
Saritha Balu, Mrinal R. Bachute, K. Venkatraman, John Augustine Parvathinathan
Biomedical Signal Processing and Control, 2025
The most prevalent condition that causes vision distortion is cataracts. The greatest method to reduce the danger and prevent blindness is to detect cataracts accurately and timely detection. Research interest in the cataract detection systems based on artificial intelligence is recently increased. In this manuscript, Optimized Dynamic Global Structure Enhanced Multi-channel Graph Neural Network depend Automatic Cataract Disease Classification (DSEMGNN-CACD-SETOA) is proposed. The input fundus images are obtained using glaucoma dataset and the image is given to pre-processing. The input images are pre-preprocessing utilizing Generalized Multi-kernel Maximum Correntropy Kalman Filter (GMMCKF) to resize and normalize the image. The pre-processed imagery is provided to the categorization. Finally, the pre-processed imageries are provided to Dynamic Global Structure Enhanced multi-channel Graph Neural Network (DSEMGNN) to classify cataract disease as Referable Glaucoma and Non-referable Glaucoma. The Stock Enhancing Trading Optimization Algorithm (SETOA) is proposed for improving the weight parameter of DSEMGNN for cataract disease classification. The proposed KOAC-GCIGNN-AcME-SBOA technique is implemented on Python. The proposed approach attains 28%, 30.78% and 25.29% higher accuracy, 15.08%, 20.58%, and 15.25% higher precision when comparing with the existing methods like GLA-Net: A global–local attention network for automated cataract categorization (GLAN-CNN-ACC), cataract grading technique depending on deep convolutional neural networks and stacking ensemble learning (CGM-DCNN-SEL),automated cataract detection scheme with deep learning for fundus imageries (ACD-DNN-FI)respectively.
Integration of metaheuristic based feature selection with ensemble representation learning models for privacy aware cyberattack detection in IoT environments
M. Karthikeyan, R. Brindha, Maria Manuel Vianny, V. Vaitheeshwaran, Mrinal Bachute, Sanket Mishra, Bibhuti Bhusan Dash
Scientific Reports, 2025
The Internet of Things (IoT) connects virtual and physical objects inserted with software, devices, and other technology that interchange data utilizing the Internet. It enables diverse devices and individuals to exchange data, interconnect, and personalize services to ease usage. Despite IoT’s merits, rising cyberthreats and the rapid growth of smart devices increase the risk of data breaches and security attacks. The increasing complexity of cyberattacks demands advanced intrusion detection systems (IDS) to defend crucial assets and data. AI techniques such as machine learning (ML) and deep learning (DL) have shown robust potential in improving IDS performance by accurately detecting and classifying malicious network behavior in IoT environments. This manuscript proposes an Adaptive Metaheuristic-Based Feature Selection with Ensemble Learning Model for Privacy-Preserving Cyberattack Detection (AMFS-ELPPCD) technique. The data normalization stage initially applies Z-score normalization to convert input data into a beneficial format. The AMFS-ELPPCD model utilizes the adaptive Harris hawk optimization (AHHO) model for the feature process selection of the subset. Furthermore, ensemble models such as bidirectional gated recurrent unit (BiGRU), Wasserstein autoencoder (WAE), and deep belief network (DBN) are used for the classification process. Finally, social group optimization (SGO) optimally adjusts the ensemble classifiers’ hyperparameter values, resulting in better classification performance. A set of simulations is performed to exhibit the promising results of the AMFS-ELPPCD under dual datasets. The experimental validation of the AMFS-ELPPCD technique portrayed a superior accuracy value of 99.44% and 98.85% under the CICIDS-2017 and NSLKDD datasets over existing models.
A deep dive into artificial intelligence with enhanced optimization-based security breach detection in internet of health things enabled smart city environment
S Jayanthi, Sodagudi Suhasini, N. Sharmili, E. Laxmi Lydia, V. Shwetha, Bibhuti Bhusan Dash, Mrinal Bachute
Scientific Reports, 2025
Internet of Health Things (IoHT) plays a vital role in everyday routine by giving electronic healthcare services and the ability to improve patient care quality. IoHT applications and devices become widely susceptible to cyber-attacks as the tools are smaller and varied. Additionally, it is of dual significance once IoHT contains tools applied in the healthcare field. In the context of smart cities, IoHT enables proactive health management, remote diagnostics, and continuous patient monitoring. Therefore, it is essential to advance a strong cyber-attack detection method in the IoHT environments to mitigate security risks and prevent devices from being vulnerable to cyber-attacks. So, improving an intrusion detection system (IDS) for attack identification and detection using the IoHT method is fundamentally necessary. Deep learning (DL) has recently been applied in attack detection because it can remove and learn deeper features of known attacks and identify unknown attacks by analyzing network traffic for anomalous patterns. This study presents a Securing Attack Detection through Deep Belief Networks and an Advanced Metaheuristic Optimization Algorithm (SADDBN-AMOA) model in smart city-based IoHT networks. The main aim of the SADDBN-AMOA technique is to provide a resilient attack detection method in the IoHT environment of smart cities to mitigate security threats. The data pre-processing phase applies the Z-score normalization method for converting input data into a structured pattern. For the selection of the feature process, the proposed SADDBN-AMOA model designs a slime mould optimization (SMO) model to select the most related features from the data. Followed by the deep belief network (DBN) method is used for the attack classification method. Finally, the improved Harris Hawk optimization (IHHO) approach fine-tunes the hyperparameter values of the DBN method, leading to superior classification performances. The effectiveness of the SADDBN-AMOA method is investigated under the IoT healthcare security dataset. The experimental validation of the SADDBN-AMOA method illustrated a superior accuracy value of 98.71% over existing models.
Melanoma skin cancer detection and classification using cycle-consistent simplicial adversarial attention adaptation networks with banyan tree growth optimization in medical image processing
Arvind Kumar Shukla, Gaurav Agrawal, R. Giri Prasad, Mrinal Bachute
Biomedical Signal Processing and Control, 2025
Dual attention holographic convolutional neural network based Ictal and Interictal States of automatic seizure detection using multi-channel scalp EEG with Humboldt squid optimization algorithm
Harikrishna Mulam, V.P. Rameshkumaar, Mrinal Bachute, Elangovan Muniyandy
Smart Health, 2025
Insights of semantic segmentation using the DeepLab architecture for autonomous driving
Javed Subhedar, Mrinal R Bachute
Methodsx, 2025
One of the critical tasks of autonomous driving systems is the Perception task (detecting the surroundings), which involves semantic Segmentation. The vital computer vision task of semantic segmentation assigns a "label" to every pixel in the input image. "Semantic segmentation" task consists of partitioning scenes as seen by the Autonomous Vehicle into several communicative slices by categorizing and labelling all image pixel for semantics. This paper gives insights into DeepNet V3 + architecture with ResNet50V2 as the backbone and the other as EfficientNetv2 backbone for feature extraction. The impact of the Squeeze and Excitation module and the Convolutional Block Attention Module is also compared for these architectures for semantic segmentation using the CAMVid data set. All six models are evaluated for Categorical Accuracy and mIoU metrics. The maximum Categorical Accuracy of 97.25 % was achieved in the model ResNet50V2 as the backbone and the Mean IoU of 80.56 %•Feature extraction using DeepNet V3 + architecture with ResNet50V2 and EfficientNetv2 as the backbone.•Insights of using the Squeeze and Excitation and Convolutional Block Attention Module for the DeepNet V3 + architecture.
A Novel Machine Learning and Sensor-Driven System for Nondestructive Detection of Jaggery Adulteration
Vinayak Bairagi, Vaishali H. Kamble, Sharad T Jadhav, Mrinal R Bachute
IEEE Sensors Letters, 2025
Food adulteration is a major challenge on a global scale impacting 10% of the food supply and leading to financial losses up to $30–40 billion annually. A developing country, such as India, is also not an exception to this widespread concerning issue and has significant adulteration cases reported across various categories, including Jaggery, which is its major product sharing 55% of the total world Jaggery production. While the literature reports a few methods for detecting various food adulterations, jaggery—the most popular food in India—has received meagre attention. Moreover, the reported methods have limited success and need further experimentation on a variety of diverse datasets before they are practically deployable. This research presents a classical, novel color-based method for detecting the adulteration in the jaggery. A color sensor is used to detect the color of melted jaggery samples, and an Arduino Uno is used to further analyze the color for reliable detection of adulteration. This research exploits the direct relationship between the captured pixel intensities of the jaggery and its purity to develop a linear regression model. The developed product is validated using samples having varying percentages of adulterations (10%–70%) caused due to single and multiple adulterants (sugar and food color) in jaggery. The machine learning-based novel approach developed in this research gives promising results with an accuracy of 94.67% and a precision as 92.6%. The developed method for identifying tampered jaggery is user-friendly, affordable, portable, and nondestructive and the experimental results confirm its superiority.
Medicinal Plant Recognition Using Image Processing
Vedanti Arekar, Devashree Vaishampayan, Mrinal Bachute, Ketan Kotecha, Ajith Abraham
Lecture Notes in Networks and Systems, 2025
A comprehensive approach for waste management with GAN-augmented classification
Yashashree Mahale, Nida Khan, Kunal Kulkarni, Shilpa Gite, Biswajeet Pradhan, Abdullah Alamri, Chang-Wook Lee, Nandhini K., Mrinal Bachute
Peerj Computer Science, 2025
Image processing and computer vision highly rely on data augmentation in machine learning models to increase the diversity and variability within training datasets for better performance. One of the most promising and widely used applications of data augmentation is in classifying waste object images. This research focuses on augmenting waste object images with generative adversarial networks (GANS). Here deep convolutional GAN (DCGAN), an extension of GAN is utilized, which uses convolutional and convolutional-transpose layers for better image generation. This approach helps generate realism and variability in images. Furthermore, object detection and classification techniques are used. By utilizing ensemble learning techniques with DenseNet121, ConvNext, and Resnet101, the network can accurately identify and classify waste objects in images, thereby contributing to improved waste management practices and environmental sustainability. With ensemble learning, a notable accuracy of 99.80% was achieved. Thus, by investigating the effectiveness of these models in conjunction with data augmentation techniques, this novel approach of GAN-based augmentation cooperated with ensemble models aims to provide valuable insights into optimizing waste object identification processes for real-world applications. Future work will focus on better data augmentation methods with other types of GANS architectures and introducing multimodal sources of data to further increase the performance of the classification and detection models.
Multimodal Gas Detection Using E-Nose and Thermal Images: An Approach Utilizing SRGAN and Sparse Autoencoder
Pratik Jadhav, Vuppala Adithya Sairam, Niranjan Bhojane, Abhyuday Singh, Shilpa Gite, Biswajeet Pradhan, Mrinal Bachute, Abdullah Alamri
Computers Materials and Continua, 2025
Non-Linear Control of Interleaved Boost Converter Using Disturbance Observer-Based Approach
Avinash Mishra, Sanjoy Mandal, Jean-Yves Dieulot, Mrinal R. Bachute, Mohd Faizan, Anna Pinnarelli, Mehrdad Heidari, Alireza Soleimani
IEEE Access, 2025
Neutrosophic Sets in Big Data Analytics: A Novel Approach for Feature Selection and Classification
Ravi Ravi, , , , , , , , , , Ahmed Dheyaa Radhi, Lateef Abd Zaid Qudr, Harshavardhan Reddy Penubadi, Ravi Sekhar, Pritesh Shah, Mrinal Bachute, Jamal Fadhil Tawfeq, Hassan muwafaq Gheni
International Journal of Neutrosophic Science, 2025
Optimisation of semantic segmentation algorithm for autonomous driving using U-NET architecture
Javed Subhedar, Mrinal R. Bachute, Ketan Kotecha
Iaes International Journal of Artificial Intelligence, 2024
Comparative Long-Term Electricity Forecasting Analysis: A Case Study of Load Dispatch Centres in India
Saikat Gochhait, Deepak K. Sharma, Mrinal Bachute
Iraqi Journal for Electrical and Electronic Engineering, 2024
A review of GAN-synthesized brain MR image applications
Ankita Tiwari, Sampada Tavse, Mrinal Bachute, Abhishek Bhola
Revolutionizing AI with Brain Inspired Technology Neuromorphic Computing, 2024
Performance Analysis of Advanced Encryption Standards for Voice Cryptography with Multiple Patterns
Firas Hazzaa, Akram Qashou, Israa Ibraheem Al Barazanchi, Ravi Sekhar, Pritesh Shah, Mrinal Bachute, Azmi Shawkat Abdulbaqi
International Journal of Safety and Security Engineering, 2024
Artificial taste perception of tea beverage using machine learning
Amruta Bajirao Patil, Mrinal Rahul Bachute
Artificial Intelligence Machine Learning and User Interface Design, 2024
CNN-CatBoost ensemble deep learning model for enhanced disease detection and classification of kidney disease
Navaneeth Bhaskar, Ratnaprabha Ravindra Borhade, Sheetal Barekar, Mrinal Bachute, Vinayak Bairagi
Indonesian Journal of Electrical Engineering and Computer Science, 2024
On Board Charger for Electric Vehicle
Vaishnavi Kulkarni, Mrinal Bachute, Rahul Bachute
Iet Conference Proceedings, 2024
Nodules Detection in Lungs CT Images Using Improved YOLOV5 and Classification of Types of Nodules by CNN-SVM
Aparna M. Harale, Vinayak K. Bairagi, Ekkarat Boonchieng, Mrinal R. Bachute
IEEE Access, 2024
HCLA_CBiGRU: Hybrid convolutional bidirectional GRU based model for epileptic seizure detection
Milind Natu, Mrinal Bachute, Ketan Kotecha
Neuroscience Informatics, 2023
Speech Subscribers Using Python
Rika Mallika, Mrinal Bachute
Iet Conference Proceedings, 2023
Remote Sensing Potato Plant Disease Image Reconstruction: Leveraging Deep Learning Object Detection
Ajay Veneesh Nelson B, Sowmya Vedantam, Mrinal R Bachute
Proceedings of the 2023 International Conference on Innovative Computing Intelligent Communication and Smart Electrical Systems Icses 2023, 2023
Color Detection Using Python
Mrinal Bachute, Aditya Singh, Akash Kumar, Aman Sahil
Iet Conference Proceedings, 2023
QR Video Steganography Using Python
Sasane Atharva Suresh, Srujan Borkar, Prithvi Majety, Mrinal Bachute
Iet Conference Proceedings, 2023
Image and Video Steganography using Least Significant Bit Technique
Akshya Ganeshan Iyer, Anshika Srivastava, Mitali Pagarware, Mrinal Bachute
Iet Conference Proceedings, 2023
Text Enhancement by Image Processing in MATLAB
Amber Sahu, Sambhav Bhansali, Kushagra Sachwani, Sumit Dharmamehar, Mrinal Bachute
Iet Conference Proceedings, 2023
Challenges and Limitations in Speech Recognition Technology: A Critical Review of Speech Signal Processing Algorithms, Tools and Systems
Sneha Basak, Himanshi Agrawal, Shreya Jena, Shilpa Gite, Mrinal Bachute, Biswajeet Pradhan, Mazen Assiri
CMES Computer Modeling in Engineering and Sciences, 2023
A Systematic Literature Review on Applications of GAN-Synthesized Images for Brain MRI
Sampada Tavse, Vijayakumar Varadarajan, Mrinal Bachute, Shilpa Gite, Ketan Kotecha
Future Internet, 2022
Framework for Deep Learning-Based Language Models Using Multi-Task Learning in Natural Language Understanding: A Systematic Literature Review and Future Directions
Rahul Manohar Samant, Mrinal R. Bachute, Shilpa Gite, Ketan Kotecha
IEEE Access, 2022
Indian Sign Language Detection using Tensor Flow and Deep Learning
H. Sabunwala, R. Kurhe, A. Sarkar, A. Khetan, M. Bachute
Iet Conference Proceedings, 2022
Retraction:Review on Epileptic Seizure Prediction: Machine Learning and Deep Learning Approaches
Milind Natu, Mrinal Bachute, Shilpa Gite, Ketan Kotecha, Ankit Vidyarthi
Computational and Mathematical Methods in Medicine, 2022
Autonomous Driving Architectures: Insights of Machine Learning and Deep Learning Algorithms
Mrinal R. Bachute, Javed M. Subhedar
Machine Learning with Applications, 2021
Identification and classification of the tea samples by using sensory mechanism and arduino uno
Amruta Patil, Mrinal Bachute, Ketan Kotecha
Inventions, 2021
Fabrication of 100nm Nano Pillars on Silicon
Journal of Nanostructures, 2021
Classification of alzheimer’s disease patients using texture analysis and machine learning
Sumit Salunkhe, Mrinal Bachute, Shilpa Gite, Nishad Vyas, Saanil Khanna, Keta Modi, Chinmay Katpatal, Ketan Kotecha
Applied System Innovation, 2021
Removing the Noise from X-ray Image using Image Processing Technology: A Bibliometric Survey and Future Research Directions
Library Philosophy and Practice, 2021
A Bibliometric Analysis on Recent Classification Techniques for Alzheimer’s Disease
Library Philosophy and Practice, 2021
A Bibliometric Analysis of the Tea Quality Evaluation using Artificial Intelligence
Library Philosophy and Practice, 2021
Artificial Perception of the Beverages: An In-Depth Review of the Tea Sample
Amruta Bajirao Patil, Mrinal Rahul Bachute, Ketan Kotecha
IEEE Access, 2021
Correction: Artificial perception of the beverages: An in-depth review of the tea sample (IEEE Access (2021) 9 (82761-82785) DOI: 10.1109/ACCESS.2021.3086038)
Amruta Bajirao Patil, Mrinal Rahul Bachute, Ketan Kotecha
IEEE Access, 2021
Alzheimer's Disease Detection and Classification using Machine Learning Techniques
M. Bachute, N. Vyas, S. Khanna, K. Modi, C. Katpatal
Iet Conference Proceedings, 2021
Implementation of Convolutional Neural Network for Covid-19 Diagnosis
M. Bachute, N. Maheswari, L. Narang, A. Sunil, S. Kewlani
Iet Conference Proceedings, 2021
Manipulating visible signal with plasmonic-based optical transistor: A new application to amplification
IS Amiri, G Palai, Mrinal Bachute, Gupteswar Sahu
Optik, 2019
Analysis of least mean square and recursive least squared adaptive filter algorithm for speech enhancement application
Mrinal Bachute, R. D. Kharadkar
Communications in Computer and Information Science, 2018
Speech based human machine interaction system for home automation
Mayur Katore, M. R. Bachute
2015 IEEE Bombay Section Symposium Frontiers of Technology Fuelling Prosperity of Planet and People Ibss 2015, 2016

RECENT SCHOLAR PUBLICATIONS

Automatic Diverse Epileptic Seizure Detection Model Using SWA-LeNet
MA Natu, MR Bachute
Circuits, Systems, and Signal Processing 45 (2), 1286-1317 , 2026
2026
Optimized dynamic global structure enhanced multi-channel graph neural network based automatic cataract disease classification
S Balu, MR Bachute, K Venkatraman, JA Parvathinathan
Biomedical Signal Processing and Control 110, 108125 , 2025
2025
Citations: 1
Melanoma skin cancer detection and classification using cycle-consistent simplicial adversarial attention adaptation networks with banyan tree growth optimization in medical …
AK Shukla, G Agrawal, RG Prasad, M Bachute
Biomedical Signal Processing and Control 108, 107914 , 2025
2025
Citations: 7
A comprehensive approach for waste management with GAN-augmented classification
Y Mahale, N Khan, K Kulkarni, S Gite, B Pradhan, A Alamri, CW Lee, ...
PeerJ Computer Science 11, e3156 , 2025
2025
Citations: 2
Dual attention holographic convolutional neural network based Ictal and interictal states of automatic seizure detection using multi-channel scalp EEG with Humboldt squid …
H Mulam, VP Rameshkumaar, M Bachute, E Muniyandy
Smart Health, 100599 , 2025
2025
Citations: 3
A deep dive into artificial intelligence with enhanced optimization-based security breach detection in Internet of Health Things enabled smart city environment
S Jayanthi, S Suhasini, N Sharmili, E Laxmi Lydia, V Shwetha, BB Dash, ...
Scientific Reports 15 (1), 22909 , 2025
2025
Citations: 6
Integration of metaheuristic based feature selection with ensemble representation learning models for privacy aware cyberattack detection in IoT environments
M Karthikeyan, R Brindha, MM Vianny, V Vaitheeshwaran, M Bachute, ...
Scientific Reports 15 (1), 22887 , 2025
2025
Citations: 7
Insights of semantic segmentation using the deeplab architecture for autonomous driving
J Subhedar, MR Bachute
MethodsX 14, 103387 , 2025
2025
Citations: 3
Strengthening cloud data protection based on a novel cyber security framework
A Mhana, FK Jabor, GA Omran, JF Tawfeq, AD Radhi, VK Harpale, ...
Applied Data Science and Analysis 2025, 155-164 , 2025
2025
Citations: 4
A Novel Machine Learning and Sensor-Driven System for Non-Destructive Detection of Jaggery Adulteration
V Bairagi, VH Kamble, ST Jadhav, MR Bachute
IEEE Sensors Letters , 2025
2025
Citations: 4
Non-linear control of interleaved boost converter using disturbance observer-based approach
A Mishra, S Mandal, JY Dieulot, MR Bachute, M Faizan, A Pinnarelli, ...
IEEE Access 13, 23833-23840 , 2025
2025
Citations: 12
Multimodal Gas Detection Using E-Nose and Thermal Images: An Approach Utilizing SRGAN and Sparse Autoencoder
P Jadhav, VA Sairam, N Bhojane, A Singh, S Gite, B Pradhan, M Bachute, ...
Comput. Mater. Contin 83, 3493-3517 , 2025
2025
Citations: 2
Neutrosophic sets in big data analytics: a novel approach for feature selection and classification
AS Abdulbaqi, AD Radhi, LAZ Qudr, HR Penubadi, R Sekhar, P Shah, ...
Int. J. Neutrosophic Sci 25 (1) , 2025
2025
Citations: 11
A review of gan-synthesized brain mr image applications
A Tiwari
Exploring Generative Adversarial Networks and Meta-Learning Synergies, 1-56 , 2025
2025
Citations: 1
On board charger for electric vehicle
V Kulkarni, M Bachute, R Bachute
8th IET Smart Cities Symposium (SCS 2024) 2024, 866-871 , 2024
2024
Optimisation of semantic segmentation algorithm for autonomous driving using U-NET architecture
J Subhedar, MR Bachute, K Kotecha
IAES Int. J. Artif. Intell.(IJ-AI) 13 (4), 3987 , 2024
2024
Citations: 1
Performance Analysis of Advanced Encryption Standards for Voice Cryptography with Multiple Patterns.
F Hazzaa, A Qashou, II Al Barazanchi, R Sekhar, P Shah, M Bachute, ...
International Journal of Safety & Security Engineering 14 (5) , 2024
2024
Citations: 6
Nodules detection in lungs CT images using improved YOLOV5 and classification of types of nodules by CNN-SVM
AM Harale, VK Bairagi, E Boonchieng, MR Bachute
IEEE Access 12, 140456-140471 , 2024
2024
Citations: 13
Artificial Taste Perception of Tea Beverage Using Machine Learning
AB Patil, MR Bachute
Artificial Intelligence, Machine Learning and User Interface Design, 1-26 , 2024
2024
Citations: 1
CNN-CatBoost ensemble deep learning model for enhanced disease detection and classification of kidney disease
M Bachute
Indonesian journal of electrical engineering and computer science , 2024
2024
Citations: 4

MOST CITED SCHOLAR PUBLICATIONS

Autonomous driving architectures: insights of machine learning and deep learning algorithms
MR Bachute, JM Subhedar
Machine Learning with Applications 6, 100164 , 2021
2021
Citations: 449
Framework for deep learning-based language models using multi-task learning in natural language understanding: A systematic literature review and future directions
RM Samant, MR Bachute, S Gite, K Kotecha
IEEE Access 10, 17078-17097 , 2022
2022
Citations: 159
Challenges and limitations in speech recognition technology: A critical review of speech signal processing algorithms, tools and systems
S Basak, H Agrawal, S Jena, S Gite, M Bachute, B Pradhan, M Assiri
CMES-Computer Modeling in Engineering and Sciences , 2023
2023
Citations: 86
HCLA_CBiGRU: Hybrid convolutional bidirectional GRU based model for epileptic seizure detection
M Natu, M Bachute, K Kotecha
Neuroscience Informatics 3 (3), 100135 , 2023
2023
Citations: 40
A systematic literature review on applications of GAN-synthesized images for brain MRI
S Tavse, V Varadarajan, M Bachute, S Gite, K Kotecha
Future Internet 14 (12), 351 , 2022
2022
Citations: 26
Classification of Alzheimer’s disease patients using texture analysis and machine learning
S Salunkhe, M Bachute, S Gite, N Vyas, S Khanna, K Modi, C Katpatal, ...
Applied System Innovation 4 (3), 49 , 2021
2021
Citations: 25
Speech based human machine interaction system for home automation
M Katore, MR Bachute
2015 IEEE Bombay Section Symposium (IBSS), 1-6 , 2015
2015
Citations: 24
Identification and classification of the tea samples by using sensory mechanism and Arduino UNO
A Patil, M Bachute, K Kotecha
Inventions 6 (4), 94 , 2021
2021
Citations: 22
Artificial Perception of the Beverages: An in-depth Review of the Tea sample
AB Patil, MR Bachute, K Kotecha
IEEE Access 9, 82761-82785 , 2021
2021
Citations: 16
Automatic speech and speaker recognition by mfcc, hmm and vector quantization
SD Deshmukh, MR Bachute
International Journal of Engineering and Innovative Technology (IJEIT) 3 (1 … , 2013
2013
Citations: 15
Nodules detection in lungs CT images using improved YOLOV5 and classification of types of nodules by CNN-SVM
AM Harale, VK Bairagi, E Boonchieng, MR Bachute
IEEE Access 12, 140456-140471 , 2024
2024
Citations: 13
Non-linear control of interleaved boost converter using disturbance observer-based approach
A Mishra, S Mandal, JY Dieulot, MR Bachute, M Faizan, A Pinnarelli, ...
IEEE Access 13, 23833-23840 , 2025
2025
Citations: 12
Neutrosophic sets in big data analytics: a novel approach for feature selection and classification
AS Abdulbaqi, AD Radhi, LAZ Qudr, HR Penubadi, R Sekhar, P Shah, ...
Int. J. Neutrosophic Sci 25 (1) , 2025
2025
Citations: 11
Smart and Innovative Trends in Next Generation Computing Technologies: Third International Conference, NGCT 2017, Dehradun, India, October 30-31, 2017, Revised Selected Papers …
P Bhattacharyya, HG Sastry, V Marriboyina, R Sharma
Springer , 2018
2018
Citations: 10
Melanoma skin cancer detection and classification using cycle-consistent simplicial adversarial attention adaptation networks with banyan tree growth optimization in medical …
AK Shukla, G Agrawal, RG Prasad, M Bachute
Biomedical Signal Processing and Control 108, 107914 , 2025
2025
Citations: 7
Integration of metaheuristic based feature selection with ensemble representation learning models for privacy aware cyberattack detection in IoT environments
M Karthikeyan, R Brindha, MM Vianny, V Vaitheeshwaran, M Bachute, ...
Scientific Reports 15 (1), 22887 , 2025
2025
Citations: 7
Real time object detection and tracking using Raspberry Pi
DV Madhekar, MR Bachute
Int. J. Eng. Sci. Comput 7 (6) , 2017
2017
Citations: 7
A deep dive into artificial intelligence with enhanced optimization-based security breach detection in Internet of Health Things enabled smart city environment
S Jayanthi, S Suhasini, N Sharmili, E Laxmi Lydia, V Shwetha, BB Dash, ...
Scientific Reports 15 (1), 22909 , 2025
2025
Citations: 6
Performance Analysis of Advanced Encryption Standards for Voice Cryptography with Multiple Patterns.
F Hazzaa, A Qashou, II Al Barazanchi, R Sekhar, P Shah, M Bachute, ...
International Journal of Safety & Security Engineering 14 (5) , 2024
2024
Citations: 6
Speech denoising using wavelet transform
TA Lonare, MR Bachute
IOSR Journal of VLSI and Signal Processing (IOSR-JVSP) 6 (3), 36-41 , 2016
2016
Citations: 6

Dr Mrinal Bachute

EDUCATION

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Scopus Publications

RECENT SCHOLAR PUBLICATIONS

MOST CITED SCHOLAR PUBLICATIONS