Dr.K.BOOPALAN

@aitsrajampet.com

Professor /Computer Science and Engineering
Annamacharya Institute of Technology & Sciences, Rajampet

Dr.K.BOOPALAN


               Download Compact PDF  
https://researchid.co/boopalank
Dr. K.BOOPALAN is working as an Professor in the Computer Science And Engineering Program of ANNAMACHARYA INSTITUTE OF TECHNOLOGY AND SCIENCES(AUTONOMOUS) RAJAMPET - 516126 , KADAPA DT , AP, India & He is completed his Ph.D (CSE) in Oct 2018. He is completed his M.E Degree in April 2005 in the field of Computer Science and Engineering. and his B.E. Degree in 1999 in the field of Computer Science and Engineering

EDUCATION

B.E,M.E,P.hD,
Computer Science and Engineering

RESEARCH INTERESTS

Data Mining , IoT , Sentiment Analysis
12

Scopus Publications

201

Scholar Citations

4

Scholar h-index

3

Scholar i10-index

Scopus Publications

  • Multimodal deep feature fusion with transformer for brain tumor classification from magnetic resonance imaging
    M. Pajany, K. Boopalan, R. Rajesh, W. JaiSingh, Bibhuti Bhusan Dash, Saroja Kumar Rout, P. Pavan Kumar
    Scientific Reports, 2026
    Brain tumors (BTs) arise due to abnormal cell growth, which has a high mortality rate globally. Millions of lives can be saved through the timely identification of BT. Precise identification and segmentation of BTs are essential to enhance the precision of analysis and the efficiency of therapeutic strategies. Magnetic resonance imaging (MRI) is a broadly utilized analytical tool. Furthermore, deep learning (DL) has recently shown efficiency in addressing several computer vision tasks. Several DL-driven methods are implemented for BT segmentation and attained impressive outcomes. This study presents a Multimodal Deep Feature Fusion Framework for Automated Brain Tumor Detection and Segmentation (MDFF-ABTDS) model. This objective is to develop a multimodal DL that integrates feature fusion and transformer networks for the precise detection and segmentation of BTs from medical images. Initially, image pre-processing is performed using Contrast Limited Adaptive Histogram Equalization (CLAHE) and image normalization. Feature extraction is carried out through fusion models such as CapsNet, ResNet-50, and AlexNet. These extracted features are then passed to a bi-directional convolutional long short-term memory combined with transformer (TBConvL-Net) models to classify tumors and non-tumors effectively. Finally, the tumor is classified to identify its location using the nnUNet model for a precise segmentation process. A series of experimental analyses of the MDFF-ABTDS method portrayed a superior accuracy value of 98.91% over existing models under the BT MRI dataset.
  • Optimal Energy Management of Hybrid PV–Wind–Battery Microgrids Using Deep Reinforcement Learning–Based Markov Decision Processes
    Tharani Padmanaban, Rajeshwari Ramaiah Murugesan, T. Sathiya, Kaleeswari Balasubramanian, K. Boopalan, Arun Anthonisamy, Bibhu Prasad Ganthia
    Journal of Engineering Science and Technology Review, 2026
    The growing penetration of renewable sources in the current power systems has led to the need of smart management of energy in order to achieve stability, reliability, and peak operation of power.This paper introduces a superior design of Intelligent Energy Management of Hybrid PV-Wind-Battery Microgrids based on Deep Reinforcement Learning (DRL)-Enhanced Markov Decision Processes (MDP).The suggested solution uses the sequential decision-making ability of MDPs and the adaptive learning performance of DRL to minimize power flow, use of energy storage and optimality of cost given varying environmental and load scenarios.The DRL agent can discover an optimal energy management policy based on its ability to model the stochastic character of the renewable generation and demand, and ensures that the cost of operation has a minimum without compromising the reliability of the system.The framework includes real time information regarding solar irradiance, wind speed and battery state-of-charge in order to take proactive control decisions to maintain constant power balance and stability of the grid.The evidence of simulation shows that the DRL-improved MDP methodology is superior to the conventional rule-based and static optimization algorithms regarding the convergence rate, energy saving, and uncertainty resistance.This smart hybrid architecture provides a scalable and flexible platform and solution to next generation microgrid operation allowing increased renewable energy infiltration and aiding the process of transitioning to sustainable, autonomous smart energy systems.
  • Tabular Transformer vs Ensemble Baselines for Early Lung Cancer Detection
    K. Boopalan, K. Lokeshwaran, S. Vasudevan, Pajany M
    2025 4th International Conference on Smart Technologies and Systems for Next Generation Computing Icstsn 2025, 2025
    Lung cancer continues to be one of the most serious health challenges worldwide, with late diagnosis often leading to poor survival outcomes. Recent progress in artificial intelligence and machine learning has created new possibilities for building accurate and data-driven prediction systems. In this study, a comparative framework was developed using both classical models and a transformer-based deep learning approach, applied on a structured dataset of cancer risk factors. Feature selection was carried out with a genetic algorithm, followed by optimization of transformer hyperparameters through ant colony optimization. Standard classifiers such as logistic regression, support vector machine, random forest and XGBoost were also tested for benchmarking. Results indicate that traditional ensemble methods achieved slightly better accuracy and ROC-AUC compared to the optimized transformer, although the transformer remained competitive and consistent. The work highlights the promise of hybrid strategies, combining feature engineering and advanced architectures, for enhancing early detection of lung cancer.
  • Advanced Facial Emotion Recognition Using DCNN-ELM: A Comprehensive Approach to Preprocessing, Feature Extraction and Performance Evaluation
    K. Boopalan, Satyajee Srivastava, K R Kavitha, D. Usha Rani, K. Jayaram Kumar, et al.
    Journal of Computer Science, 2025
    As a subfield of affective computing, Facial Emotion Recognition (FER) teaches computers to read people's facial expressions to determine their emotional state. Because facial expressions convey 55% of an individual's emotional and mental state in the whole range of face-to-face communication, Facial Emotion Recognition is crucial for connecting humans and computers. Improvements in the way computer systems (robotic systems) interact with or assist humans are another benefit of advancements in this area. Deep learning is key to the highly advanced research being conducted in this area. Recently, FER research has made use of Ekman's list of fundamental emotions as one of these models. Anger, Disgust, Fear, Happy, Sad, Surprise, and Neutral are the seven main emotions mapped out on Robert Plutchik's wheel. Opposite to each of the main emotions is its polar opposite. There are four steps to the suggested method: Preprocessing, feature extraction, model performance evaluation, and finalization. The preprocessing step makes use of the kernel filter. The proposed approach uses SWLDA for feature extraction. Facial Emotion Recognition (FER) is critical for improving human-computer interactions, particularly in educational settings. This study presents a novel hybrid approach combining Deep Convolutional Neural Networks (DCNN) with Extreme Learning Machines (ELM) to enhance emotion recognition accuracy. The proposed model demonstrates superior performance compared to traditional DCNN and standalone ELM approaches, offering real-time emotion detection in online learning environments. The effectiveness of the model is validated using publicly available datasets, setting a new benchmark for FER. This study makes major contributions to the field of Facial Emotion Recognition (FER) by offering a robust architecture that combines Deep Convolutional Neural Networks (DCNN) with Extreme Learning Machines (ELM). The methodology's efficacy is proven with publicly available datasets, establishing a new standard in FER, particularly in educational settings.
  • Computer Aided Based Performance Analysis of Glioblastoma Tumor Detection Methods using UNET-CNN
    Sasirekha N, S. Prabu, Tatiraju.V.Rajani Kanth V, Chitra D, Boopalan K, B. Buvaneswari
    International Journal of Computational and Experimental Science and Engineering, 2024
    Brain tumors are the life killing and threatening disease which affects all age groups around the world. The timely detection and followed by the perspective treatments saves the human life. The tumor regions in brain are detected and segmented using UNET-CNN architecture in this paper. During training process of the proposed work, both Glioblastoma and Healthy brain Magnetic Resonance Imaging (MRI) is preprocessed and then multi level transform is applied on the preprocessed image. The features are further computed from the transformed coefficients and these features are trained by UNET-CNN architecture to obtain trained vectors. During testing process of the proposed work, the test brain MRI image is preprocessed and then decomposed coefficients are obtained by multi level transform. Features are computed from these decomposed coefficients and they are classified using UNET-CNN architecture with the trained vectors. The simulation results of the developed methodology are compared with similar studies on both BRATS 2017 and BRATS 2018 datasets
  • Analysis of android malware detection using machine learning techniques
    K. Abrar Ahmed, K. Boopalan, K. Lokeshwaran, R. Sugumar, C. Kotteeswaran
    Aip Conference Proceedings, 2024
    Malware is the term for harmful software that hackers use to infiltrate specific machines or an entire network of an organization. It takes advantage of flaws in legitimate software (such as a browser or plug in for an online application) that can be hijacked. According to a recent survey, 80% of smartphones were still vulnerable to malware attacks that might jeopardize personal information, identity, and business or financial information. Android mobile malware is the biggest threat to data security. To examine the efforts surrounding malware risks on Android mobile devices, a range of machine learning algorithms, including Naive Bayes, Support Vector Machine, K Nearest Neighbor, and Random Forest, are employed in this research. Each algorithm is evaluated according to various performance criteria to determine which algorithms are better suited to detecting malicious software. According to experimental findings, Support Vector Machine and Random Forest deliver the best results, making them the most efficient methods for malware detection.
  • Intelligent Solar Energy Harvesting and Management in IoT Nodes Using Deep Self-Organizing Maps
    Anita Rajkumar Shinkar, Drumil Joshi, R V S Praveen, Yelisela Rajesh, Boopalan K, Dharmendra Singh
    2nd International Conference on Emerging Research in Computational Science Icercs 2024, 2024
    Smart city energy generation must be efficient and dependable. As a result of studies conducted in this field, reliable control schemes for microgrid management have been developed, which seamlessly integrate with smart building management systems. In order for a building microgrid's solar energy system to recover from problems, this article suggests reliable controllers and the hardware to install them. Training models and Internet of Things (IoT) sensors provide the backbone of this proposed approach. When it comes to organizational and industrial contexts, sensors have evolved significantly with the introduction of the IoT. Pressure, optical, temperature, chemical sensors, and proximity are just a few examples of the many types of data that IoT devices may collect and send through sensor networks, allowing for greater efficiency. The model achieves a 91.37% accuracy rate in solar energy harvesting prediction after being trained using the Extended DSOM.
  • Enabling security in MANETs using an efficient cluster based group key management with elliptical curve cryptography in consort with sail fish optimization algorithm
    C. Shanmuganathan, K. Boopalan, G. Elangovan, P.J. Sathish Kumar
    Transactions on Emerging Telecommunications Technologies, 2023
    Abstract In MANET, group key management is a vital part of multicast security. But distribution of keys in an authenticated manner is a difficult task in group key management. The existing methods provide low security with high processing time during group key management resulting does not provide sufficient results. Therefore, enabling security in MANETs using an efficient cluster based group key management with elliptical curve cryptography in consort with sail fish optimization algorithm is proposed in this article for two‐level security with reduced computational overhead. At first, all the nodes in the cluster are structured in hierarchy method. The key server creates public key utilizing private key of the group node. Here, elliptical curve cryptography based meta‐heuristic sail fish optimization algorithm is used to select the optimal private key for better secure communication. After selecting this optimum private key, the key server creates the public key, and a common group key is created using this generated public keys. If the nodes joint or exit from the subgroup, the reset process is executed in group key management process. Finally, this process reduces the computational overhead of rekeying method. The proposed method is simulated by Python programming and network simulator‐3. The proposed elliptical curve cryptography based sail fish optimization algorithm attains 10.9%, 22.21%, and 11.43% low computational overhead, 19.34%, 13.45%, and 42.13% low latency, 43.45%, 22.21%, and 12.22% high packet delivery rate, 11.23%, 13.41%, and 21.11% high network life time than the existing methods.
  • A Short Overview on Various Bio-Inspired Algorithms
    K. Boopalan, C. Shanmuganathan, K. Lokeshwaran, T. Balaji
    Lecture Notes in Networks and Systems, 2023
  • Forecasting of Stock Volatility using Deep Learning Model with Likelihood-Based Loss Function
    K. Boopalan, S Yamunadevi, K Chanthirasekaran, Ahmed Mudassar Ali, R Anbunathan, Pundru Chandra Shaker Reddy
    Proceedings of IEEE 2023 5th International Conference on Advances in Electronics Computers and Communications Icaecc 2023, 2023
    Predicting the volatility of financial assets can be helpful, and volatility is employed in a variety of financial contexts. Volatility in the stock sector is a widely used indicator of overall market risk. Time-series anticipating in the economic sector is exceedingly difficult because of the intricacy and fluctuation of financial fields. In this paper, we anticipate stock index volatility using a deep-neural-network (DNN) and a long-short-term-memory (LSTM) model. Distance loss function is widely used in related research to train machine-learning (ML) strategies, although this has two drawbacks. The initial is that their designs cannot be reasonably contrasted to econometric ones, and the second is that they create mistakes when utilizing predicted volatility as the estimated aim. We also incorporate a probability-based loss-function (LF) to train the deep-learning (DL) approaches and evaluate the models based on the likelihood of the test sample, which allows us to address the aforementioned two issues. In comparison to the econometric and the DL strategies with distance LF, our results show that the volatility can be predicted more accurately by the LSTM model in the two DL approaches with likelihood-based LF.
  • Mining opinions about traffic status using twitter messages
    International Journal of Civil Engineering and Technology, 2017
  • Traffic prediction and forecasting using classification of twitter stream analysis
    International Journal of Control Theory and Applications, 2016

RECENT SCHOLAR PUBLICATIONS

  • Multimodal deep feature fusion with transformer for brain tumor classification from magnetic resonance imaging
    M Pajany, K Boopalan, R Rajesh, W JaiSingh, BB Dash, SK Rout, ...
    Scientific Reports , 2026
    2026
  • A Self-Supervised and Explainable Framework for Zero-Day Attack Detection and Mitigation in Windows Firewall
    S Vasudevan, PGS Saketh, U Suraj, K Boopalan
    2026 International Conference on Future and Advanced Computing Technologies … , 2026
    2026
  • Retraction notice to “Autonomous Service for Managing Real Time Notification in Detection of Covid-19 Virus”[Computers and Electrical Engineering 101 (2022) 108117]
    YM Abd Algani, K Boopalan, G Elangovan, DT Santosh, ...
    Computers and Electrical Engineering, 111053 , 2026
    2026
  • Tabular Transformer vs Ensemble Baselines for Early Lung Cancer Detection
    K Boopalan, K Lokeshwaran, S Vasudevan
    2025 Fourth International Conference on Smart Technologies and Systems for … , 2025
    2025
  • IOT AND THE FUTURE OF WASTE MANAGEMENT IN SMART URBAN ENVIRONMENT
    K Boopalan, PD Kumar, TVN Rohith, VA Kumar
    Dinesh and Kumar, P. Dinesh and Rohith, TVN and Rohith, TVN and Kumar, V … , 2025
    2025
  • Machine Learning-Driven Facial Recognition for Student Attendance Monitoring
    K Boopalan, PM Krishna, PA Chowdary, BAS Varshith, S Suchitra
    International Conference on Intelligent Systems and Digital Transformation … , 2025
    2025
  • Intelligent solar energy harvesting and management in IoT nodes using deep self-organizing maps
    AR Shinkar, D Joshi, RVS Praveen, Y Rajesh, D Singh
    2024 International Conference on Emerging Research in Computational Science … , 2024
    2024
    Citations: 124
  • Computer Aided Based Performance Analysis of Glioblastoma Tumor Detection Methods using UNET-CNN
    SNS to Sasirekha N., PSS to Prabu S., TVSRKTV Rajani Kanth, ...
    2024
  • Analysis of android malware detection using machine learning techniques
    KA Ahmed, BKS to Boopalan K., LK c, SR d, KC c
    2024
    Citations: 4
  • Forecasting of Stock Volatility using Deep Learning Model with Likelihood-Based Loss Function
    K Boopalan, S Yamunadevi, K Chanthirasekaran, AM Ali, R Anbunathan, ...
    2023 IEEE Fifth International Conference on Advances in Electronics … , 2023
    2023
    Citations: 3
  • Enabling security in MANETs using an efficient cluster based group key management with elliptical curve cryptography in consort with sail fish optimization algorithm
    C Shanmuganathan, K Boopalan, G Elangovan, PJ Sathish Kumar
    Transactions on Emerging Telecommunications Technologies 34 (3), e4717 , 2023
    2023
    Citations: 7
  • A Short Overview on Various Bio-Inspired Algorithms
    K Boopalan, C Shanmuganathan, K Lokeshwaran, T Balaji
    Machine Learning in Information and Communication Technology: Proceedings of … , 2022
    2022
    Citations: 3
  • Autonomous service for managing real time notification in detection of COVID-19 virus
    YM Abd Algani, K Boopalan, G Elangovan, DT Santosh, ...
    Computers and Electrical Engineering 101, 108117 , 2022
    2022
    Citations: 13
  • Efficient detection of location based routing attack by routing attack mitigation in mobile agent systems
    C Shanmuganathan, K Boopalan
    Annals of the Romanian Society for Cell Biology 25 (5), 673-681 , 2021
    2021
    Citations: 1
  • HIV SmART; HIV Healthcare
    K Boopalan, S Kalaimagal
    IN Patent 14,227 , 2019
    2019
  • Mining Opinions About Traffic Status Using Twitter Messages
    K Boopalan, C Nalini, A Rajesh
    International Journal of Civil Engineering and Technology (IJCIET) Vol 8 … , 2017
    2017
    Citations: 2
  • Traffic Prediction and forecasting using classification of twitter streem analysis
    K Boopalan, A Rajesh
    International journal of control theory and Applications 9 (28), 319 -324 , 2016
    2016
  • Traffic Prediction and forecasting using classification of twitter streem analysis
    k Boopalan
    International Conference on Recent Advances in Technology, Engineering and … , 2016
    2016
  • Web based Traffic sentiment Analysis
    K Boopalan
    Conference on Recent Trends (RTEECE’15) , 2015
    2015
  • similarly facial images using unsupervised label refinement
    K Boopalan
    Conference on Recent Trends in Communication Engineering (RTEECE ’15 ) , 2015
    2015

MOST CITED SCHOLAR PUBLICATIONS

  • Intelligent solar energy harvesting and management in IoT nodes using deep self-organizing maps
    AR Shinkar, D Joshi, RVS Praveen, Y Rajesh, D Singh
    2024 International Conference on Emerging Research in Computational Science … , 2024
    2024
    Citations: 124
  • Potent antimicrobial activity of Rhizophora mucronata
    S Kusuma, PA Kumar, K Boopalan
    Journal of Ecobiotechnology , 2012
    2012
    Citations: 44
  • Autonomous service for managing real time notification in detection of COVID-19 virus
    YM Abd Algani, K Boopalan, G Elangovan, DT Santosh, ...
    Computers and Electrical Engineering 101, 108117 , 2022
    2022
    Citations: 13
  • Enabling security in MANETs using an efficient cluster based group key management with elliptical curve cryptography in consort with sail fish optimization algorithm
    C Shanmuganathan, K Boopalan, G Elangovan, PJ Sathish Kumar
    Transactions on Emerging Telecommunications Technologies 34 (3), e4717 , 2023
    2023
    Citations: 7
  • Analysis of android malware detection using machine learning techniques
    KA Ahmed, BKS to Boopalan K., LK c, SR d, KC c
    2024
    Citations: 4
  • Forecasting of Stock Volatility using Deep Learning Model with Likelihood-Based Loss Function
    K Boopalan, S Yamunadevi, K Chanthirasekaran, AM Ali, R Anbunathan, ...
    2023 IEEE Fifth International Conference on Advances in Electronics … , 2023
    2023
    Citations: 3
  • A Short Overview on Various Bio-Inspired Algorithms
    K Boopalan, C Shanmuganathan, K Lokeshwaran, T Balaji
    Machine Learning in Information and Communication Technology: Proceedings of … , 2022
    2022
    Citations: 3
  • Mining Opinions About Traffic Status Using Twitter Messages
    K Boopalan, C Nalini, A Rajesh
    International Journal of Civil Engineering and Technology (IJCIET) Vol 8 … , 2017
    2017
    Citations: 2
  • Efficient detection of location based routing attack by routing attack mitigation in mobile agent systems
    C Shanmuganathan, K Boopalan
    Annals of the Romanian Society for Cell Biology 25 (5), 673-681 , 2021
    2021
    Citations: 1
  • Multimodal deep feature fusion with transformer for brain tumor classification from magnetic resonance imaging
    M Pajany, K Boopalan, R Rajesh, W JaiSingh, BB Dash, SK Rout, ...
    Scientific Reports , 2026
    2026
  • A Self-Supervised and Explainable Framework for Zero-Day Attack Detection and Mitigation in Windows Firewall
    S Vasudevan, PGS Saketh, U Suraj, K Boopalan
    2026 International Conference on Future and Advanced Computing Technologies … , 2026
    2026
  • Retraction notice to “Autonomous Service for Managing Real Time Notification in Detection of Covid-19 Virus”[Computers and Electrical Engineering 101 (2022) 108117]
    YM Abd Algani, K Boopalan, G Elangovan, DT Santosh, ...
    Computers and Electrical Engineering, 111053 , 2026
    2026
  • Tabular Transformer vs Ensemble Baselines for Early Lung Cancer Detection
    K Boopalan, K Lokeshwaran, S Vasudevan
    2025 Fourth International Conference on Smart Technologies and Systems for … , 2025
    2025
  • IOT AND THE FUTURE OF WASTE MANAGEMENT IN SMART URBAN ENVIRONMENT
    K Boopalan, PD Kumar, TVN Rohith, VA Kumar
    Dinesh and Kumar, P. Dinesh and Rohith, TVN and Rohith, TVN and Kumar, V … , 2025
    2025
  • Machine Learning-Driven Facial Recognition for Student Attendance Monitoring
    K Boopalan, PM Krishna, PA Chowdary, BAS Varshith, S Suchitra
    International Conference on Intelligent Systems and Digital Transformation … , 2025
    2025
  • Computer Aided Based Performance Analysis of Glioblastoma Tumor Detection Methods using UNET-CNN
    SNS to Sasirekha N., PSS to Prabu S., TVSRKTV Rajani Kanth, ...
    2024
  • HIV SmART; HIV Healthcare
    K Boopalan, S Kalaimagal
    IN Patent 14,227 , 2019
    2019
  • Traffic Prediction and forecasting using classification of twitter streem analysis
    K Boopalan, A Rajesh
    International journal of control theory and Applications 9 (28), 319 -324 , 2016
    2016
  • Traffic Prediction and forecasting using classification of twitter streem analysis
    k Boopalan
    International Conference on Recent Advances in Technology, Engineering and … , 2016
    2016
  • Web based Traffic sentiment Analysis
    K Boopalan
    Conference on Recent Trends (RTEECE’15) , 2015
    2015