Sreedhar Kollem

@sru.edu.in

SR University



                 

https://researchid.co/sreeswap1221

EDUCATION

B. Tech, M. Tech, Ph.D.

72

Scopus Publications

926

Scholar Citations

16

Scholar h-index

23

Scholar i10-index

Scopus Publications

  • Distended pixel replacement method for enhancing visual quality of low-light images
    G. Sekar, Arun Sekar Rajasekaran, Sreedhar Kollem, and Babji Prasad Chapa
    Springer Science and Business Media LLC

  • Heart abnormality classification using ECG and PCG recordings with novel PJM-DJRNN
    Nadikatla Chandrasekhar, Sujatha Canavoy Narahari, Sreedhar Kollem, Samineni Peddakrishna, Archana Penchala, and Babji Prasad Chapa
    Elsevier BV

  • EfficientNetB3-DTL: Classification of diabetic retinopathy images using modified EfficientNetB3 with deep transfer learning


  • Intelligent diagnosis support system for screening diabetes subjects using hybrid machine learning algorithms



  • Emergency Vehicle Classification Using Combined Temporal and Spectral Audio Features with Machine Learning Algorithms
    Dontabhaktuni Jayakumar, Modugu Krishnaiah, Sreedhar Kollem, Samineni Peddakrishna, Nadikatla Chandrasekhar, and Maturi Thirupathi
    MDPI AG
    This study presents a novel approach to emergency vehicle classification that leverages a comprehensive set of informative audio features to distinguish between ambulance sirens, fire truck sirens, and traffic noise. A unique contribution lies in combining time domain features, including root mean square (RMS) and zero-crossing rate, to capture the temporal characteristics, like signal energy changes, with frequency domain features derived from short-time Fourier transform (STFT). These include spectral centroid, spectral bandwidth, and spectral roll-off, providing insights into the sound’s frequency content for differentiating siren patterns from traffic noise. Additionally, Mel-frequency cepstral coefficients (MFCCs) are incorporated to capture the human-like auditory perception of the spectral information. This combination captures both temporal and spectral characteristics of the audio signals, enhancing the model’s ability to discriminate between emergency vehicles and traffic noise compared to using features from a single domain. A significant contribution of this study is the integration of data augmentation techniques that replicate real-world conditions, including the Doppler effect and noise environment considerations. This study further investigates the effectiveness of different machine learning algorithms applied to the extracted features, performing a comparative analysis to determine the most effective classifier for this task. This analysis reveals that the support vector machine (SVM) achieves the highest accuracy of 99.5%, followed by random forest (RF) and k-nearest neighbors (KNNs) at 98.5%, while AdaBoost lags at 96.0% and long short-term memory (LSTM) has an accuracy of 93%. We also demonstrate the effectiveness of a stacked ensemble classifier, and utilizing these base learners achieves an accuracy of 99.5%. Furthermore, this study conducted leave-one-out cross-validation (LOOCV) to validate the results, with SVM and RF achieving accuracies of 98.5%, followed by KNN and AdaBoost, which are 97.0% and 90.5%. These findings indicate the superior performance of advanced ML techniques in emergency vehicle classification.



  • Night vision security patrolling robot
    Y. Srikanth, Ch. Rajendra Prasad, S. Srinvas, Sreedhar Kollem, and Rajesh Thota
    AIP Publishing

  • Farmer friendly robotic vehicle
    Ch. Rajendra Prasad, Srikanth Yalabaka, Sreedhar Kollem, Srinivas Samala, and P. Ramchandar Rao
    AIP Publishing

  • A study on energy efficiency of a wireless communication system
    Srinivas Samala, Ch. Rajendra Prasad, Sreedhar Kollem, Srikanth Yalabaka, and P. Ramchandar Rao
    AIP Publishing


  • Non-invasive glucose prediction and classification using NIR technology with machine learning
    M. Naresh, V. Siva Nagaraju, Sreedhar Kollem, Jayendra Kumar, and Samineni Peddakrishna
    Elsevier BV

  • Smart health prediction using machine learning
    Ch. Rajendra Prasad, Pillalamarri Shivapriya, Naragani Bhargavi, Nagaraj Ravula, Supraja Lakshmi Devi Sripathi, and Sreedhar Kollem
    AIP Publishing

  • Autonomous Vehicle Accident Detection with Event Data Recording for Accident Analysis
    Zuber Basha Shaik, Jayakumar Dontabhaktuni, Saketi Bhavani, Chilumuri Dharani, Samineni Peddakrishna, Sreedhar Kollem, and Prashanth Kumar B
    IEEE
    The increasing occurrence of traffic accidents necessitates innovative solutions to enhance vehicle safety and improve accident analysis. This paper presents the development of an Event Data Recorder (EDR) system specifically designed for autonomous vehicles, utilizing Internet of Things (IoT) technology. The system integrates various sensors, including 3-axis gyroscope and a 3-axis accelerometer, flame sensors, smoke sensors and collision switches with a microcontroller equipped with Bluetooth and Wi-Fi capabilities. The microcontroller manages data collection, processing and communication, capturing essential information such as vehicle movement, orientation, fire risks, smoke detection and collision impacts. The system's decision-making algorithm triggers alerts based on sensor thresholds and collision detection. In the event of an accident, the system utilizes a GPS module to transmit precise location data (latitude and longitude) to emergency contacts and medical facilities via the Blynk application. The Blynk application also provides remote access to accident and sensor data for further analysis. Initial testing of the prototype demonstrated its ability to accurately detect and respond to simulated accident conditions, indicating its potential for real-world application. By enabling timely medical intervention and detailed accident analysis, it plays a crucial role in advancing vehicle safety.

  • An Optimized GAN Approach for Denoising MRI Brain Tumor Images
    Sreedhar Kollem, Samineni Peddakrishna, and Chandrasekhar Sirigiri
    IEEE
    Image denoising is a critical pre-processing step in MRI brain imaging that is intended to improve image quality and clarity by reducing noise. The presence of noise in MRI images can be attributed to multiple factors, including the imaging apparatus, patient motion, and transmission inaccuracies. The presence of this noise might hinder the visibility of crucial anatomical features, hence posing challenges for medical practitioners in effectively diagnosing and interpreting the images. An optimized technique has been presented to tackle the aforementioned problem, which combines the Generative Adversarial Network (GAN) with the Spotted Hyena Optimizer (SHO). This technique is known as an optimized GAN (OGAN). The utilization of the Spotted Hyena Optimizer (SHO) in conjunction with GAN-based denoising presents a sophisticated method for enhancing image quality. The SHO optimizes the GAN's parameters, resulting in high-quality denoised images that retain essential characteristics. The effectiveness of this approach has been evaluated using peak signal-to-noise ratio (PSNR), Universal Image Quality Index (UQI), and Structural Similarity Index (SSIM). This methodology exhibits its superior performance compared to existing techniques.

  • A Smart Pulse Oximeter for Remote Patient Monitoring and Critical Alerts
    Sai Prajith Kancharla, Nikhil Gummadavelly, Uday Nuvvula, Nadikatla Chandrasekhar, Samineni Peddakrishna, and Sreedhar Kollem
    Springer Nature Switzerland

  • A fine-tuned deep transfer learning model in classifying multiclass brain tumors for preclinical MRI image analysis
    Ch. Rajendra Prasad, Sreedhar Kollem, Srinivas Samala, Ramu Moola, Srikanth Yalabaka, and Ravichander Janapati
    Elsevier

  • An Optimal Review of Deep Learning Algorithms for Object Detection
    Patteti Krishna, Sreedhar Kollem, and BV Devendra Rao
    IEEE
    Computer vision, a vital facet of deep learning, seamlessly integrates object localization and classification techniques to attain precise outcomes. Deep learning, nestled within the broader realm of machine learning, leverages neural networks as its fundamental algorithm. Particularly in the domain of images and computer vision, convolutional neural networks (CNNs) reign supreme. Over the years, numerous researchers have innovated state-of-the-art models by tailoring the foundational CNN architecture in diverse manners. This paper undertakes a comparative analysis of several models, evaluating their performance based on metrics such as mean average precision (MAP) or accuracy across various datasets. Additionally, we intend to quantify their computational complexity, measured in floating-point operations (FLOPs), employing a TensorFlow profiler.

  • Innovative Approach: CNNs for Heart Disease Prediction
    Sreedhar Kollem, Patteti Krishna, Samineni Peddakrishna, and BV Devendra Rao
    IEEE
    The fact that heart disease is still a health concern emphasizes the necessity of precise and effective techniques for early identification and diagnosis. The aim of this work is to investigate the general behavior of most machine learning algorithms. Analyzing a dataset comprising a spectrum of demographic data from people both with and without cardiac disease forms part of the study. Our suggested approach intends to assess and compare, for heart disease risk, the predictive capability of machine learning techniques. Among the methods under study are decision trees, random forests, support vector machines, logistic regression, k closest neighbors, and suggested CNN. The results suggest that various machine learning algorithms show levels of capabilities. Random Forests display accuracy and reliability while CNNs demonstrate their potential in capturing connections within the data and occasionally achieving higher accuracy than Random Forests. Support Vector Machines and Logistic Regression also exhibit performance. This study adds to the expanding research on predicting heart disease by providing an analysis of machine learning techniques.

  • Diabetic-Retinopathy Classification Using a Modified VGG16 Model
    Ch. Rajendra Prasad, J. Saikrishna, K. Akash, K. Sai Ganesh, Sreedhar Kollem, and Chakradhar Adupa
    IEEE
    Diabetes has an impact on the eyes and may cause diabetic retinopathy (DR). It happens when the blood capillaries in the retina, the tissue that reacts to light at the back of the eye, are damaged, resulting in blindness. However, due to slow progression, the disease shows few signs in the early stages, hence making disease detection a difficult task. Therefore, a fully automated system is required to support the detection and screening process at early stages. In this paper, an automated DR classification using a modified VGG16 model was proposed. The proposed system employed DR 224 by 224 Gaussian Filtered dataset images. These images are pre-processed before applying to the VGG 16 pretrained model. The pre-trained VGG 16 model employed for the classification of DR images. The proposed model achieves an accuracy of 91.11 % and a loss of 20.17%. The proposed model helps physicians identify specific class DR.

  • Lane Detection with U-Net Optimization through Hyperparameter Tuning and Cross Validation
    Dontabhaktuni Jayakumar, Simran Saikia, Shaik Yasmin Roshni, Samineni Peddakrishna, Sreedhar Kollem, M Naresh, and Modugu Krishnaiah
    IEEE
    The implementation of secure technology in intelligent vehicles is essential, particularly with lane detection playing a crucial role in autonomous driving systems for enhanced navigation and traffic management efficiency. This paper presents a methodology for optimizing a UNet-based model for lane detection. The UNet architecture is utilized for image segmentation tasks, with an extensive evaluation of model performance conducted using various metrics. The training of the model involves fine-tuning hyperparameters within a specified search space that includes learning rates, batch sizes, and dropout rates. By iteratively adjusting these parameters based on validation loss, we aim to identify the most effective hyperparameters that will enhance the model’s performance. Additionally, a five-fold cross-validation is conducted to ensure the model’s robustness and ability to generalize to different datasets. The final model is constructed based on the best hyperparameters obtained from the tuning process. Throughout the training process, the model demonstrates a high level of accuracy, achieving a training accuracy of $\\mathbf{9 9. 9 3 \\%}$ and a corresponding loss of $\\mathbf{0. 0 0 3 1}$. Furthermore, the validation accuracy stands at an impressive 99.81% with a validation loss of 0.01. The model achieves a high accuracy of $\\mathbf{9 9. 7 8 \\%}$ on the test set, with an IoU score of 0.8861 and an F1 score of 0.9396. These metrics suggest that the model effectively learns and generalizes proficiently to new, unseen data.

  • An Effective PDE-based Thresholding for MRI Image Denoising and H-FCM-based Segmentation
    Sreedhar Kollem, Samineni Peddakrishna, P Joel Josephson, Sridevi Cheguri, Garaga Srilakshmi, and Y Rama Lakshmanna
    International Journal of Experimental Research and Review
    Image denoising and segmentation play a crucial role in computer graphics and computer vision. A good image-denoising method must effectively remove noise while preserving important boundaries. Various image-denoising techniques have been employed to remove noise, but complete elimination is often impossible. In this paper, we utilize Partial Differential Equation (PDE) and generalised cross-validation (GCV) within Adaptive Haar Wavelet Transform algorithms to effectively denoise an image, with the digital image serving as the input. After denoising, the image is segmented using the Histon-related fuzzy c-means algorithm (H-FCM), with the processed image serving as the output. The proposed method is tested on images exposed to varying levels of noise. The performance of image denoising and segmentation techniques is evaluated using metrics such as Peak Signal-to-Noise Ratio (PSNR) of 77.42, Mean Squared Error (MSE) of 0.0011, and Structural Similarity Index (SSIM) of 0.7848. Additionally, segmentation performance is measured with a sensitivity of 99%, specificity of 98%, and an accuracy of 98%. The results demonstrate that the proposed methods outperform conventional approaches in these metrics. The implementation of the proposed methods is carried out on the MATLAB platform.

  • Optimizing Energy Storage Systems: Interplay of Current, Voltage, and Temperature Parameters in Batteries
    R. Suganya, Leo Joseph, and Sreedhar Kollem
    IEEE
    Energy storage systems in electric vehicles come across boundaries interrelated to perilous parameters. There are challenging factors like charging infrastructure, constrained energy density which affects driving range, and battery degradation. The proposed system studies lithium-ion batteries' energy storage ability by considering three parameters: current, voltage, and temperature. The proposed model is simulated using MATLAB/ Simulink and studies the interplay of the considered parameters and is observed to be the energy-storing technique with their graphical analysis. The three-parameter outperforms the capacity of energy storage by its values that are not exceeded and limited to the ideal values which yields superior results, also essential for sustainable renewable energy sources, also for grid applications.

  • Comprehensive CNN Model for Brain Tumour Identification and Classification using MRI Images
    Ch.Rajendra Prasad, Kodakandla Srividya, Kaparthi Jahnavi, Teppa Srivarsha, Sreedhar Kollem, and Srikanth Yelabaka
    IEEE
    Brain tumours are critical malignancies that develop as a result of aberrant cell division. Typically, tumour classification involves a biopsy, which is conducted after the final brain operation. Technological advances have facilitated the utilization of medical imaging by physicians to diagnose a wide range of symptoms within the domain of medicine. In this project, we propose the Comprehensive CNN method for the detection and classification of brain tumours. For experimentation, we used the SARTAJ, Br35H, and Figshare datasets. This proposed model outperforms in terms of accuracy, recall, F1 score, and precision as compared to other traditional methods. This research contributes to the ongoing efforts to enhance the capabilities of medical imaging and paves the way for more accurate and efficient brain tumor analysis.

RECENT SCHOLAR PUBLICATIONS

  • Distended pixel replacement method for enhancing visual quality of low-light images
    G Sekar, AS Rajasekaran, S Kollem, BP Chapa
    Signal, Image and Video Processing 19 (3), 205 2025

  • Heart abnormality classification using ECG and PCG recordings with novel PJM-DJRNN
    N Chandrasekhar, SC Narahari, S Kollem, S Peddakrishna, A Penchala, ...
    Results in Engineering 25, 104032 2025

  • Integrating Artificial Intelligence in Electric Vehicles and Optimizing Logistics for Sustainable Transportation
    R Suganya, LMIL Joseph, S Kollem
    Cases on AI-Driven Solutions to Environmental Challenges, 385-418 2025

  • A fine-tuned deep transfer learning model in classifying multiclass brain tumors for preclinical MRI image analysis
    CR Prasad, S Kollem, S Samala, R Moola, S Yalabaka, R Janapati
    Mining Biomedical Text, Images and Visual Features for Information Retrieval 2025

  • Understanding lithium-ion battery management systems in electric vehicles: Environmental and health impacts, comparative study, and future trends: A review
    R Suganya, LMIL Joseph, S Kollem
    Results in Engineering 24, 103047 2024

  • An Optimized GAN Approach for Denoising MRI Brain Tumor Images
    S Kollem, S Peddakrishna, C Sirigiri
    2024 4th International Conference on Artificial Intelligence and Signal 2024

  • Autonomous Vehicle Accident Detection with Event Data Recording for Accident Analysis
    ZB Shaik, J Dontabhaktuni, S Bhavani, C Dharani, S Peddakrishna, ...
    2024 4th International Conference on Artificial Intelligence and Signal 2024

  • Emergency Vehicle Classification Using Combined Temporal and Spectral Audio Features with Machine Learning Algorithms
    D Jayakumar, M Krishnaiah, S Kollem, S Peddakrishna, ...
    Electronics 13 (19), 3873 2024

  • Innovative Approach: CNNs for Heart Disease Prediction
    S Kollem, P Krishna, S Peddakrishna, BVD Rao
    2024 IEEE Region 10 Symposium (TENSYMP), 1-6 2024

  • An Optimal Review of Deep Learning Algorithms for Object Detection
    P Krishna, S Kollem, BVD Rao
    2024 IEEE Region 10 Symposium (TENSYMP), 1-6 2024

  • Diabetic-Retinopathy Classification Using a Modified VGG16 Model
    CR Prasad, J Saikrishna, K Akash, KS Ganesh, S Kollem, C Adupa
    2024 International Conference on Advances in Computing Research on Science 2024

  • A novel hybrid deep CNN model for breast cancer classification using Lipschitz-based image augmentation and recursive feature elimination
    S Kollem, C Sirigiri, S Peddakrishna
    Biomedical Signal Processing and Control 95, 106406 2024

  • An efficient method for MRI brain tumor tissue segmentation and classification using an optimized support vector machine
    S Kollem
    Multimedia Tools and Applications 83 (26), 68487-68519 2024

  • Lane Detection with U-Net Optimization through Hyperparameter Tuning and Cross Validation
    D Jayakumar, S Saikia, SY Roshni, S Peddakrishna, S Kollem, M Naresh, ...
    2024 15th International Conference on Computing Communication and Networking 2024

  • Optimizing Energy Storage Systems: Interplay of Current, Voltage, and Temperature Parameters in Batteries
    R Suganya, L Joseph, S Kollem
    2024 3rd International Conference on Computational Modelling, Simulation and 2024

  • Farmer friendly robotic vehicle
    CR Prasad, S Yalabaka, S Kollem, S Samala, PR Rao
    AIP Conference Proceedings 2971 (1) 2024

  • A study on energy efficiency of a wireless communication system
    S Samala, CR Prasad, S Kollem, S Yalabaka, PR Rao
    AIP Conference Proceedings 2971 (1) 2024

  • Night vision security patrolling robot
    Y Srikanth, CR Prasad, S Srinvas, S Kollem, R Thota
    AIP Conference Proceedings 2971 (1) 2024

  • A fast computational technique based on a novel tangent sigmoid anisotropic diffusion function for image-denoising
    S Kollem
    Soft Computing 28 (11), 7501-7526 2024

  • Non-invasive glucose prediction and classification using NIR technology with machine learning
    M Naresh, VS Nagaraju, S Kollem, J Kumar, S Peddakrishna
    Heliyon 10 (7) 2024

MOST CITED SCHOLAR PUBLICATIONS

  • Enhancement of images using morphological transformation
    K Sreedhar, B Panlal
    arXiv preprint arXiv:1203.2514 2012
    Citations: 204

  • A review of image denoising and segmentation methods based on medical images
    S Kollem, KRL Reddy, DS Rao
    International Journal of Machine Learning and Computing 9 (3), 288-295 2019
    Citations: 116

  • A comprehensive review on thin film amorphous silicon solar cells
    S Sreejith, J Ajayan, S Kollem, B Sivasankari
    Silicon 14 (14), 8277-8293 2022
    Citations: 44

  • Improved partial differential equation-based total variation approach to non-subsampled contourlet transform for medical image denoising
    S Kollem, KR Reddy, DS Rao
    Multimedia Tools and Applications 80 (2), 2663-2689 2021
    Citations: 44

  • Denoising and segmentation of MR images using fourth order non‐linear adaptive PDE and new convergent clustering
    S Kollem, KRL Reddy, DS Rao
    International Journal of Imaging Systems and Technology 29 (3), 195-209 2019
    Citations: 39

  • AlexNet‐NDTL: Classification of MRI brain tumor images using modified AlexNet with deep transfer learning and Lipschitz‐based data augmentation
    S Kollem, KR Reddy, CR Prasad, A Chakraborty, J Ajayan, S Sreejith, ...
    International Journal of Imaging Systems and Technology 33 (4), 1306-1322 2023
    Citations: 34

  • An optimized SVM based possibilistic fuzzy c-means clustering algorithm for tumor segmentation
    S Kollem, KR Reddy, DS Rao
    Multimedia Tools and Applications 80 (1), 409-437 2021
    Citations: 34

  • Modified transform‐based gamma correction for MRI tumor image denoising and segmentation by optimized histon‐based elephant herding algorithm
    S Kollem, K Rama Linga Reddy, D Srinivasa Rao
    International Journal of Imaging Systems and Technology 30 (4), 1271-1293 2020
    Citations: 29

  • Brain tumor MRI image segmentation using an optimized multi-kernel FCM method with a pre-processing stage
    S Kollem, CR Prasad, J Ajayan, V Malathy, A Subbarao
    Multimedia Tools and Applications 82 (14), 20741-20770 2023
    Citations: 28

  • Image denoising for magnetic resonance imaging medical images using improved generalized cross‐validation based on the diffusivity function
    S Kollem, K Ramalinga Reddy, D Srinivasa Rao, C Rajendra Prasad, ...
    International Journal of imaging systems and technology 32 (4), 1263-1285 2022
    Citations: 27

  • Ferroelectric field effect transistors (FeFETs): advancements, challenges and exciting prospects for next generation non-volatile memory (NVM) applications
    J Ajayan, P Mohankumar, D Nirmal, LMIL Joseph, S Bhattacharya, ...
    Materials Today Communications 35, 105591 2023
    Citations: 24

  • Towards a more theory-driven BCI using source reconstructed dynamics of EEG time-series
    R Janapati, V Dalal, R Sengupta, U Desai, PV Raja Shekar, S Kollem
    Nano Life 12 (02), 2250005 2022
    Citations: 20

  • Image denoising by using modified SGHP algorithm
    S Kollem, KRL Reddy, DS Rao
    International Journal of Electrical and Computer Engineering 8 (2), 971 2018
    Citations: 18

  • Biodegradable sensors: A comprehensive review
    S Sreejith, LMIL Joseph, S Kollem, VT Vijumon, J Ajayan
    Measurement 219, 113261 2023
    Citations: 17

  • A novel diffusivity function-based image denoising for MRI medical images
    S Kollem, KR Reddy, DS Rao
    Multimedia Tools and Applications 82 (21), 32057-32089 2023
    Citations: 17

  • Enlargement of image based upon interpolation techniques
    KS Reddy, KRL Reddy
    International Journal of Advanced Research in Computer and Communication 2013
    Citations: 17

  • INCNet: Brain Tumor Detection using Inception and Optimization Techniques
    S Kollem, LMIL Joseph, U Desai, S Tayal, J Ajayan, S Bhattacharya, ...
    2022 International Conference on Emerging Techniques in Computational 2022
    Citations: 14

  • Image Retrieval Techniques: A Survey
    K Sreedhar
    International Journal of Electronics and Communication Engineering 9 (1), 19-27 2016
    Citations: 14

  • Breast cancer classification using CNN with transfer learning models
    CR Prasad, B Arun, S Amulya, P Abboju, S Kollem, S Yalabaka
    2023 International Conference for Advancement in Technology (ICONAT), 1-5 2023
    Citations: 13

  • Telugu Optical Character Recognition Using Deep Learning
    G Suresh, CR Prasad, S Kollem
    2022 3rd International Conference for Emerging Technology (INCET), 1-6 2022
    Citations: 13