Dr.Ch.Rajendra Prasad

Dr. Ch. Rajendra Prasad presently working as an Assistant. Professor at the Department of Electronics and Communication Engineering, S R University, Warangal, Telangana, India. He is awarded Ph.D. from Koneru Lakshmaiah Education Foundation, Guntur, Andhra Pradesh, India. He has 16 years of experience in teaching and research. He published 50 international journals and attended the 16 International Conferences and he had 14 patent publications. He is completed DST project titled “Development of Adhesive Tactile Walking Surface Indicator for Elderly and Visually Impaired People” as a co-investigator. He has ISTE and IETE Life Member ships. His areas of interests are Wireless Sensor Networks, IoT and Machine Learning.

EDUCATION

2022 - Ph.D. from KL University, Vijayawada, Andhra Pradesh
2010 - M.Tech. in Embedded Systems from SR Engineering College, Warangal
2006 - B.Tech. in Electronics and Communication Engineering (ECE) from ADAMS Engineering College, Polancha, Khammam
2002 - Diploma in ECE from Govt. Polytechnic, Warangal
1999 - SSC from ZPSS Hasanparthy, Warangal

RESEARCH INTERESTS

Medical Imaging, Image Segmentation, Classification and Computer vision

Scopus Publications

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




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



A lane and curve detection using novel pre-processing with OpenCV
Srikanth Yalabaka, Aravelli Tejaswi, Acha Nethaji, Ch. Rajendra Prasad, Konne Vamshi, and Naveen Kumar
AIP Publishing



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



Cost effective portable traffic light system using Esp32
Ch. Rajendra Prasad, P. Ramchandar Rao, Ch. Bhavani, K. Sriya, P. Vyshnavi, and Srinivas Samala
AIP Publishing



CNN-Based Osteoporosis Diagnosis: A Novel Approach to Bone Fragility Analysis
Srinivas Samala, Ch.Rajendra Prasad, Peddapelli Raj Kumar, Katukojvala Akash, MD. Afreena, and Mekala Sanjay Kumar
IEEE
Osteoporosis, a bone condition that raises the possibility of fractures, is distinguished by its diminished bone density and structural deterioration. Conventional diagnostic methods, such as DEXA scans can be time-consuming and expensive despite their reliability. In this work, we developed a CNN-based approach that employs deep learning to analyze medical images to diagnose osteoporosis and facilitate the early detection of bone fragility. We conducted experiments on osteoporosis datasets that were accessible to the general population to evaluate the model's efficiency. Our proposed model outperforms conventional techniques in terms of F1 score, precision, recall, and accuracy as indicated by the findings. The objective of this work is to increase osteoporosis diagnosis accuracy and consistency for early disease intervention.


A comparison of deep learning methods and conventional methods for classification of SSVEP signals in brain-computer interface framework
Srinivas Rao Gorre, Ravichander Janapati, Ch. Rajendra Prasad, and Usha Desai
Elsevier



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



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.


A Comprehensive Analysis of ML and DL Approaches for Epileptic Seizure Prediction
Srikanth Yalabaka, B. Sneha, Ch.Rajendra Prasad, B. Vineetha, S. Srinith, and N. Revanth
IEEE
Epilepsy is a health condition that affects many individuals, potentially causing significant harm to the brain. Seizures, a common symptom of epilepsy, can result in severe injuries. Early detection of seizures is crucial to minimizing the impact of these injuries. A seizure prediction system aims to recognize when a seizure is likely to occur, facilitating timely intervention for individuals with epilepsy. In order to predict epileptic seizures, we used machine learning (ML) and deep learning (DL) models in this work. The machine learning methods used are SVM, Random Forest, Decision Tree, KNN, and Logistic Regression. Conventional neural networks, artificial neural networks, convolutional neural networks, recurrent neural networks, and long short-term memory are the DL algorithms used. Of these models, the Long Short-Term Memory deep learning model outperforms in terms of accuracy.


Predicting Real-Time House Prices: A Machine Learning Approach Using XGBoost Algorithm
L.M.I.Leo Joseph, Edunuri Harini Reddy, Munigala Srinidhi, Pamu Venkata Saketh, Dharamsoth Mohan, and Ch. Rajendra Prasad
IEEE
The housing market involves consideration of numerous factors by prospective buyers that may change over time. As a result, the market for real estate prediction will constantly fluctuate. Machine Learning is used to analyze and predict real estate property prices. The XGBoost algorithm is used to achieve a high accuracy in forecasting the market values. The Primary Objective is to assist users in finding a suitable price according to their preferred geographical factors. The model provides accurate predictions for future real estate prices by analyzing the previous trend and market. This study sought to predict prices in Bengaluru City using the XGBoost Algorithm. The results of this study showed that the XGBoost Model achieved a prediction accuracy of 91.77%. Such an accurate valuation tool could benefit homeowners and prospective buyers when pricing properties, without requiring the help of a real estate agent.


A Comprehensive Study on Skin Disease Classification using ML Algorithms
K. Sagar, B. Sathwika, M. Maniteja, I. Navaneeth Reddy, Ch. Rajendra Prasad, and Srikanth Yalabaka
IEEE
Skin-related illnesses are a significant worldwide health concern, and early detection is crucial to successful treatment. Traditional methods of diagnosing skin disorders rely on the expertise of dermatologists, which can be expensive and time-consuming. Moreover, in countries where dermatologists are in short supply, access to timely and accurate diagnosis may be restricted. This paper proposes a classification of skin diseases based on machine learning algorithms. The five machine learning approaches used for classification are k-nearest neighbours, random forests, decision trees, Naïve Bayes, and support vector machines. The dataset utilized in this investigation was provided by the dermatology department of Peking Union Medical College Hospital in China. The support vector machine is by far the most accurate of these models, with 91.94% accuracy.


TinyML-based Real-Time System for Detecting Falls in Elderly People
Ch.Rajendra Prasad, G V Naga Satwik, E Phaneeshwari, Ramu Moola, K Pranith, and D Rakshit Rao
IEEE
Falls are one of the leading causes of death in elderly people. This can even lead to serious injuries or, in some cases, be fatal. The main issue is not just the fall itself but the lack of timely help afterwards, which causes more health problems. That is why having a system that can detect when a fall is crucial for the elderly. In this paper, a tinyML-based real-time system for detecting falls in elderly people was presented. Machine learning seems to be a perfect approach for the proposed system since it has the capability of learning from the available data and predicting the output observing various patterns and comparing them. Machine learning is perfect for this, but it usually needs a lot of power, which small embedded devices such as smartwatches do not have. That is where tiny ML comes in. This is a lighter version of machine learning that works well on small devices even with less processing power. Using tiny ML will make our proposed model strong and efficient compared to other similar existing models, with better performance and compromised processing.


A Comparative Analysis: Breast Cancer Prediction using Machine Learning Algorithms
Srikanth Yalabaka, Vanthadpula Harshini, Ch.Rajendra Prasad, Vipul Keerthi, Janagani Avinash, and Kasanaboina Muneeshwar
IEEE
The majority of women are affected by breast cancer. Considering that the majority of them are unaware that they have breast cancer. Improving breast cancer survival rates requires early detection and treatment. Statistical models, expert knowledge and judgment, modelling and simulation, historical comparisons and analogies, and expert knowledge and judgment can all be used to forecast breast cancer. Identifying the drawbacks and limitations of non-ML predictions; developing artistic or literary interpretations of predictions; and developing hybrid approaches that combine various prediction techniques, human judgment, creative thinking, and other non-quantitative factors in making predictions are some of its limitations. Using models for machine learning Python-based application of decision tree, random forest, logistic regression, and KNN algorithms for the prediction of breast cancer. The algorithms obtain good accuracy, precision, recall, and F1-score when tested on a widely used dataset on breast cancer.


MRI based Brain Tumor classification using a Fine-tuned EfficientNetB3 Transfer Learning model
Ch. Rajendra Prasad, K. Varshamrutha, Billakanti Sindhuja, Rekulapelli Ushasree, Parimella Nikhil, and A. Chakradhar
IEEE
In this paper, the formidable task of classifying brain tumors in MRI imaging was addressed by employing an extensive compilation of brain tumor images. We showed that precisely adjusting a cutting-edge efficientNetB3 model using transfer learning considerably enhanced its performance in classifying brain cancers. the experimentation employs a hybrid dataset from the Kaggle, which is a combination of two different datasets (Kaggle brain-tumor-classification-mri and Kaggle brain-tumor). The proposed fine-tuned efficientNetB3 transfer learning model showed promising results by classifying multi-class brain tumors with a testing accuracy of 98.13%.


Diabetes Prediction using Support Vector Machine
K. Sagar, Sk. Imtiyaz, A. Arvindh, A. Shiva Nagaraju, Ch. Rajendra Prasad, and P. Kiran Kumar
IEEE
Diabetes affects people worldwide, in both developed and developing nations, and is a serious health concern. The Worldwide Diabetes Federation reports that 285 million people globally are living with diabetes at this time, and in the next 20 years, that figure is predicted to rise to 380 million. Scientists are working on a very efficient, low-cost way to identify diabetes, which is vital to treat at an early stage. Data mining techniques for accurately predicting diabetes are often tested against the machine learning lab at UCI's Pima Indian diabetes database. In this research, a classifier for diabetes detection using support vector machine (SVM) machine learning technique is proposed. The primary objective is to effectively categorize diabetes from complex medical data. The outcomes of the trial suggest that Support Vector Machine has potential for accurate diabetes diagnosis.


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.


Skin Cancer Prediction using Modified EfficientNet-B3 with Deep Transfer Learning
Ch.Rajendra Prasad, Gaddam Bilveni, Bhukya Priyanka, Chinthapally Susmitha, Dubasi Abhinay, and Sreedhar Kollem
IEEE
Skin cancer is considered to be a very perilous kind of cancer and is recognized as a significant contributor to global mortality rates. The timely identification of skin cancer offers a chance to reduce the cumulative rate of death. The primary method of diagnosing skin cancer predominantly relies on visual inspection, a technique that is known to possess limitations in terms of accuracy. There have been proposals to employ deep-learning algorithms to assist physicians in promptly and accurately detecting skin cancers. This paper presents skin cancer perdition with Deep Transfer Learning (DTL). The DTL model utilized in the proposed model is EfficientNet-B3. The dataset utilized in this study was obtained from the Kaggle skin cancer dataset. Prior to being applied to the updated EfficientNet-B3, the data undergo preprocessing techniques such as rescaling and random adjustments to brightness and/or contrast, with a range of ±20%. Prior to putting it to the DTL model, the data undergoes pre-processing and augmentation. For training 80% and for testing 20% of data is used. The proposed model's training accuracy is around 98.64%, and its validation accuracy is approximately 90.6%.


Optimizing Crop Management: Customized CNN for Autonomous Weed Identification in Farming
Srinivas Samala, Udutha Sahithi, Avunoori Bharath Kumar, Odela Sravan Kumar, Veladandi Ramya Sri, Ch. Rajendra Prasad, and Sreedhar Kollem
IEEE
The agricultural industry is increasingly adopting Deep Learning methodologies to tackle obstacles related to weed identification and categorization, with the ultimate goal of enhancing crop productivity. However, the complexity stems from the striking similarity in colours, forms, and textures between weeds and crops, specifically when they are in the process of growing. Automated and precise weed identification is of the utmost importance to minimize agricultural losses and maximize the use of resources. The analysis of the literature under review enhances comprehension of the obstacles, remedies, and prospects associated with weed identification and categorization via CNN models. To address these obstacles, we have devised a solution that entails the construction and refinement of a customized Convolutional Neural Network model. The experiment employs the Four-class weed dataset obtained from Kaggle and utilizes the Adaptive Moment Estimation optimizer during the training process. The accuracy of 96.58% is demonstrated by the proposed model in accurately identifying and categorizing weeds in the fields.


A Novel DL Structure for Brain Tumor Identification Using MRI Images
Sreedhar Kollem, Pati Harika, Janagam Vignesh, Peddoju Sairam, Adunuthula Ramakanth, Samineni Peddakrishna, Srinivas Samala, and Ch. Rajendra Prasad
IEEE
The multimodal MRI scans described in this article are used to categorize brain tumors based on their location and size. Brain tumors need to be categorized in order to assess the tumors and choose the appropriate course of treatment for each class. Many different imaging methods are used to detect brain tumors. However, because MRI does not use ionizing radiation and generates better images, it is commonly used. Using deep learning (DL), a branch of machine learning has recently demonstrated impressive results, particularly in segmentation and classifiable tasks. This paper proposes a convolutional neural network-based deep learning model (DL) that uses transfer learning and EfficientNet to classify various kinds of brain cancers using publically accessible datasets. The first divides cancers into three categories: glioma, meningioma, and pituitary tumor. Compared to conventional deep learning techniques, the suggested approach produces superior results. The Python platform can be used to complete the task.


Advancing Agriculture: Plant Disease Classification Through Cutting-Edge Deep Learning Techniques
Sreedhar Kollem, Kodari Poojitha, Naroju Brahma Chary, Pulluri Saicharan, Kampelly Anvesh, Samineni Peddakrishna, and Ch. Rajendra Prasad
IEEE
The viability of agriculture and the security of the world's food supply are seriously threatened by plant diseases. Detecting these diseases promptly and accurately is crucial for effective disease control and minimizing crop output losses. Deep learning algorithms have shown possibilities recently as a method for accurately and automatically classifying plant diseases. This research presents an innovative deep-learning framework designed for plant disease classification, incorporating transfer learning and customized convolutional neural networks (CNNs). The proposed framework comprises three main phases: data pre-processing or transfer learning, feature extraction, and disease classification. This article presents a new approach to plant disease categorization using deep learning. It combines convolutional neural networks (CNNs) with transfer learning. Through this method, plant diseases can be identified with precision and automation across diverse plant species and types of disease. This facilitates more effective disease management, safeguarding the security of the global food supply. Comparative analysis indicates that the proposed method outperforms traditional approaches, yielding superior results.


Segmentation of Brain MRI Images using Multi-Kernel FCM EHO Method
Sreedhar Kollem, Ch. Rajendra Prasad, J. Ajayan, Sreejith S., LMI Leo Joseph, and Patteti Krishna
Bentham Science Publishers Ltd.
Background: In image processing, image segmentation is a more challenging task due to different shapes, locations, image intensities, etc. Brain tumors are one of the most common diseases in the world. So, the detection and segmentation of brain tumors are important in the medical field. Objective: The primary goal of this work is to use the proposed methodology to segment brain MRI images into tumor and non-tumor segments or pixels. Methods: In this work, we first selected the MRI medical images from the BraTS2020 database and transferred them to the contrast enhancement phase. Then, we applied thresholding for contrast enhancement to enhance the visibility of structures like blood arteries, tumors, or abnormalities. After the contrast enhancement process, the images were transformed into the image denoising phase. In this phase, a fourth-order partial differential equation was used for image denoising. After the image denoising process, these images were passed on to the segmentation phase. In this segmentation phase, we used an elephant herding algorithm for centroid optimization and then applied the multi-kernel fuzzy c-means clustering for image segmentation. Results: Peak signal-to-noise ratio, mean square error, sensitivity, specificity, and accuracy were used to assess the performance of the proposed methods. According to the findings, the proposed strategy produced better outcomes than the conventional methods. Conclusion: Our proposed methodology was reported to be a more effective technique than existing techniques.

RECENT SCHOLAR PUBLICATIONS

Quantum Logistic Improved Selective Mapping based PAPR Reduction for OFDM/OQAMs Systems
CHR PRASAD, S SAMALA, S YALABAKA
Journal of Information Science and Engineering 41 (3), 601-612 2025



Human activity recognition using a bagging-based deep learning framework with convolutional capsule networks and spiking neural networks
M Ramu, A Hossain, CR Prasad
International Journal of Information Technology, 1-19 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



A comparison of deep learning methods and conventional methods for classification of SSVEP signals in brain-computer interface framework
SR Gorre, R Janapati, CR Prasad, U Desai
Brain-Computer Interfaces, 177-186 2025



CNN-Based Osteoporosis Diagnosis: A Novel Approach to Bone Fragility Analysis
S Samala, CR Prasad, PR Kumar, K Akash, MD Afreena, MS Kumar
2024 International Conference on IoT Based Control Networks and Intelligent 2024



A Comprehensive Analysis of ML and DL Approaches for Epileptic Seizure Prediction
S Yalabaka, B Sneha, CR Prasad, B Vineetha, S Srinith, N Revanth
2024 International Conference on Advances in Computing Research on Science 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



Sign Language to Speech Conversion using Esp32
S Yalabaka, SSS Shetty, CR Prasad, P Sadula, P Sothuku
2024 4th Asian Conference on Innovation in Technology (ASIANCON), 1-4 2024



A Comparative Analysis: Breast Cancer Prediction using Machine Learning Algorithms
S Yalabaka, V Harshini, CR Prasad, V Keerthi, J Avinash, K Muneeshwar
2024 Asia Pacific Conference on Innovation in Technology (APCIT), 1-7 2024



TinyML-based Real-Time System for Detecting Falls in Elderly People
CR Prasad, GVN Satwik, E Phaneeshwari, R Moola, K Pranith, DR Rao
2024 Asia Pacific Conference on Innovation in Technology (APCIT), 1-6 2024



A Comprehensive Study on Skin Disease Classification using ML Algorithms
K Sagar, B Sathwika, M Maniteja, IN Reddy, CR Prasad, S Yalabaka
2024 Asia Pacific Conference on Innovation in Technology (APCIT), 1-5 2024



Predicting Real-Time House Prices: A Machine Learning Approach Using XGBoost Algorithm
LMIL Joseph, EH Reddy, M Srinidhi, PV Saketh, D Mohan, CR Prasad
2024 Asia Pacific Conference on Innovation in Technology (APCIT), 1-4 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



MRI Based Brain Tumor classification Using a Fine-Tuned EfficientNetB3 Transfer Learning Model
CR Prasad, K Varshamrutha, B Sindhuja, R Ushasree, P Nikhil, ...
2024 5th International Conference for Emerging Technology (INCET), 1-5 2024



Diabetes Prediction using Support Vector Machine
K Sagar, S Imtiyaz, A Arvindh, AS Nagaraju, CR Prasad, PK Kumar
2024 5th International Conference for Emerging Technology (INCET), 1-4 2024



A lane and curve detection using novel pre-processing with OpenCV
S Yalabaka, A Tejaswi, A Nethaji, CR Prasad, K Vamshi, N Kumar
AIP Conference Proceedings 3072 (1) 2024



Cost effective portable traffic light system using Esp32
CR Prasad, PR Rao, C Bhavani, K Sriya, P Vyshnavi, S Samala
AIP Conference Proceedings 3072 (1) 2024



Smart health prediction using machine learning
CR Prasad, P Shivapriya, N Bhargavi, N Ravula, SLD Sripathi, S Kollem
AIP Conference Proceedings 3072 (1) 2024

MOST CITED SCHOLAR PUBLICATIONS

Internet of things based home monitoring and device control using Esp32
V Pravalika, CR Prasad
International Journal of Recent Technology and Engineering 8 (1S4), 58-62 2019
Citations: 134



Digital Watermarking: State of the Art and Research Challenges in Health Care & Multimedia Applications
E Kumaraswamy, GM Kumar, K Mahender, K Bukkapatnam, CR Prasad
IOP Conference Series: Materials Science and Engineering 981 (3), 032031 2020
Citations: 57



Patient health monitoring using IOT
AS Manoj, MA Hussain, PS Teja
Mobile health applications for quality healthcare delivery, 30-45 2019
Citations: 54



A review on hydro power plants and turbines
RK Karre, K Srinivas, K Mannan, B Prashanth, CR Prasad
AIP Conference Proceedings 2418 (1) 2022
Citations: 52



Svd based robust unsighted video watermarking technique for different attacks
E Kumaraswamy, R Vatti, G Vallathan, CR Prasad, KR Danthamala
IOP Conference Series: Materials Science and Engineering 981 (3), 032030 2020
Citations: 40



Smartphone-based human activities recognition system using random forest algorithm
V Radhika, CR Prasad, A Chakradhar
2022 International Conference for Advancement in Technology (ICONAT), 1-4 2022
Citations: 36



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



A hybrid energy-efficient routing protocol for wireless body area networks using ultra-low-power transceivers for eHealth care systems
C Rajendra Prasad, P Bojja
SN Applied Sciences 2 (12), 2114 2020
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



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: 26



Gas leakage detection and alerting system using Arduino Uno
SB Shahewaz, CR Prasad
Global Journal of Engineering and Technology Advances 5 (3), 029-035 2020
Citations: 23



Cost effective atomization of Indian agricultural system using 8051 microcontroller
M Ramu, CHR Prasad
International journal of advanced research in computer and communication 2013
Citations: 21



Digital watermarking techniques: Comparative analysis and robustness for real time applications
E Kumaraswamy, K Mahender, CR Prasad, N Govardhan, BP Yadav
AIP Conference Proceedings 2418 (1) 2022
Citations: 20



Kidney cancer detection using deep learning models
K Rajkumar, RTS Ramoju, T Balelly, S Ashadapu, CR Prasad, Y Srikanth
2023 7th International Conference on Trends in Electronics and Informatics 2023
Citations: 18



A Reliable, Energy Aware and Stable Topology for Bio-sensors in Health-care Applications.
CR Prasad, P Bojja
J. Commun. 14 (5), 390-395 2019
Citations: 18



Internet of things based pollution tracking and alerting system
S Kumar, PR Rao, CR Prasad
International Journal of Innovative Technology and Exploring Engineering 8 2019
Citations: 18



Experimental investigation on road safety system at crossings
O Anusha, CR Prasad
International Journal of Engineering and Advanced Technology 8 (2), 214-18 2019
Citations: 17



A Deep Learning Model for Traffic Sign Detection and Recognition using Convolution Neural Network
MP Reddy, MDF Mohiuddin, S Budde, G Jayanth, CR Prasad, S Yalabaka
2022 2nd International Conference on Intelligent Technologies (CONIT), 1-5 2022
Citations: 16



A non-linear mathematical model-based routing protocol for WBAN-based health-care systems
C Rajendra Prasad, P Bojja
International Journal of Pervasive Computing and Communications 17 (5), 447-461 2021
Citations: 16



A review on bio-inspired algorithms for routing and localization of wireless sensor networks
P Rajendra Prasad, C. , Bojja
Journal of Advanced Research in Dynamical and Control Systems 9 (Special 2017
Citations: 15

GRANT DETAILS

A DST Project worth 89 Lakhs Titled "Development of Adhesive Tactile Walking Surface Indicator for Elderly and Visually Impaired People" Has been completed (2016-2019) Reference Id: SEED/TIDE/035/2015.

RESEARCH OUTPUTS (PATENTS, SOFTWARE, PUBLICATIONS, PRODUCTS)

Patent Publications:
1. A WEARABLE OBSTACLE DETECTOR•
2. VARIABLE LEVEL LIQUID DISPENSER•
3. INTRAVENOUS BAG ALERTING AND MONITORING SYSTEM AND METHOD•
4. SYSTEM AND METHOD FOR ALERTING A THIRD-PARTY SERVICE PROVIDER FOR FOOD REQUIREMENT
5. TRAFFIC SIGNAL CROSS MONITORING SYSTEM AND METHOD
6..POLLUTION MONITORING AND ALERTING SYSTEM AND METHOD
7. SYSTEM AND METHOD FOR MANAGING IRRIGATION OF CROPS
8. Electric Scooter
9. SYSTEM AND METHOD FOR FACE RECOGNITION
10.Social Distancing Detector for Covid-19
11. Smart Street Light System
12. Smart Home Management System
13. System and Method for Management of Hotel Operations
14.Method of Routing Health Data in a wireless Body Bio-Sensor Network
15. SMART PILL BOX