Dr.Ch.Rajendra Prasad
@sru.edu.in
Assistant Professor, Department of ECE
SR University
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
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Srikanth Yalabaka, Aravelli Tejaswi, Acha Nethaji, Ch. Rajendra Prasad, Konne Vamshi, and Naveen Kumar
AIP Publishing
Ch. Rajendra Prasad, Pillalamarri Shivapriya, Naragani Bhargavi, Nagaraj Ravula, Supraja Lakshmi Devi Sripathi, and Sreedhar Kollem
AIP Publishing
Ch. Rajendra Prasad, P. Ramchandar Rao, Ch. Bhavani, K. Sriya, P. Vyshnavi, and Srinivas Samala
AIP Publishing
Sreedhar Kollem, Katta Ramalinga Reddy, Ch. Rajendra Prasad, Avishek Chakraborty, J. Ajayan, S. Sreejith, Sandip Bhattacharya, L. M. I. Leo Joseph, and Ravichander Janapati
Wiley
Sreedhar Kollem, Ch Rajendra Prasad, J. Ajayan, V. Malathy, and Akkala Subbarao
Springer Science and Business Media LLC
Sandip Bhattacharya, L. M. I. Leo Joseph, Sheshikala Martha, Ch. Rajendra Prasad, Syed Musthak Ahmed, Subhajit Das, Debaprasad Das, and P. Anuradha
CRC Press
Ch. Rajendra Prasad, Shayaan Hussain, B. Srinivas, Srinivas Samala, Ravichander Janapati, and Srikanth Yalabaka
IEEE
A brain tumor is characterized as an aggregation of abnormal cells within the brain. These tumors can be classified into two categories: malignant and benign. Malignant is cancerous whereas benign is not. Both tumors are very hazardous as they grow rapidly and attack different parts of the cerebrum. Even after extensive research, the cause of the brain tumor is unknown. In this paper, a VGG-19 and an Inception-Resnet V2 model are presented for detecting brain tumor by employing images of MRI scans. The dataset is gathered from Kaggle and preprocessed using Keras Image Data Generator. The VGG-19 model provided an accuracy of 99.71% and the Inception-Resnet V2 provided an accuracy of 99.28%. The proposed models performed well to achieve the task.
Ch. Rajendra Prasad, Sami Mohammed, P.Ramchander Rao, Sreedhar Kollem, Srinivas Samala, and Srikanth Yalabaka
IEEE
A brain tumour is a dangerous form of cancer that happens when cells divide in an abnormal way. Recent advances in deep learning have helped the medical imaging sector in the diagnosis of numerous diseases. This paper presents Multiclass MRI Brain Tumour Classification with Deep Transfer Learning. In the proposed model, VGG-16 is employed as a deep transfer learning model. The dataset is collected from the Kaggle brain tumour MRI dataset, which is a combination of three popular brain tumour datasets such as figshare, SARTAJ, and Br35H datasets. The data are prepossessed by rescaling and random brightness and/or contrast by ±20% before applying to the modified VGG-16 model. The proposed model employs minimum computational resources and achieves better results in accuracy, precision, recall, and F1 score.
Rajendra Ch Prasad, Sreedhar Kollem, Ravichander Janapati, Srinivas Samala, and Sandip Bhattacharya
CRC Press
Srinivas Samala, Nihal Raj Reddy, Poshala Bhuvaneshwar, Kranthi Krishpartha, Chirra Sai Varun, and Ch. Rajendra Prasad
IEEE
The growing demand for high-bandwidth wireless technologies has led to spectrum scarcity, which limits the available bandwidth for wireless communication. Licensed spectrum, however, has been proven in a number of studies to be underutilized. Cognitive radio technology is being presented as a solution to this problem since it allows unlicensed users to gain opportunistic access to licensed bands of primary users. In wireless communication systems, multipath fading can cause signal attenuation and distortion due to reflection, diffraction, and scattering. Two common models used to describe multipath fading are Nakagami and Rayleigh fading channels. Nakagami fading is a more generalized model that includes Rayleigh fading as a special case. In this work, we use an improved energy detection technique to assess the effectiveness of cooperative spectrum sensing over Nakagami-m and Rayleigh fading channels. Cooperative spectrum sensing performance is assessed using extensive simulation results.
Srinivas Rao Gorre, Ravichander Janapati, and Ch. Rajendra Prasad
IEEE
Advances in Brain-Computer Interfaces (BCIs) enable persons to control physical devices and applications using their brain signals. One common approach to designing BCI systems is to use the P300 response, a positive deflection in the EEG waveform that occurs after a relevant stimulus is presented. In this paper, we propose an improved P300-based BCI using EEG scalogram images and Deep Neural Networks (DNNs). EEG scalograms is a spectrum of the EEG signals, which are used to extract relevant features for detecting P300 responses. DNNs are capable of learning complex patterns in data, making them well-suited for analyzing EEG scalogram images. We present a five-step process for building and evaluating a P300-based BCI using EEG scalogram images and DNNs. Our proposed approach promises improved accuracy and robustness in detecting P300 responses, which can enable individuals with disabilities to control devices and applications with greater ease and efficiency.
K. Rajkumar, Ravi Teja Sri Ramoju, Tharun Balelly, Sravan Ashadapu, Ch.Rajendra Prasad, and Yalabaka Srikanth
IEEE
This study presents two types of Kidney cancer detection one is with the help of images and another one is with the help of blood test samples value. Kidney disease is condition caused either by renal disease of the kidneys. In the present study, Kidney cancer is one of the critical diseases for patient's diagnosis and classification. Early detection and good treatment can avoid or decrease the growth of cancer disease into the final stage where dialysis or renal transplantation is the only way of saving the life of the patient. And another way is with machine learning models with this model the disease at an early stage can be detected, is one of the important tasks in today's world. This research proposed kidney images detection through deep learning models like Convolutional Neural Networks (CNNs), and blood samples dataset values through Artificial Neural Network (ANN) models that can be helpful for the early diagnosis of cancer. The existing studies have mainly used only simple CNN models and have done another classification of kidney images. This research consists of CNN with more convolution layers for classifying images of cancer kidneys and normal kidneys and ANN is used for kidney cancer prediction using dataset values. This research will be helpful for early and accurate diagnosis of kidney cancer to save the lives of many patients. Lastly, there is an application page that contains a code in the backend that predicts whether a person is suffering from a kidney cancer or not.
Yalabaka Srikanth, Meghana Daddanala, Manchala Sushrith, Pranith Akula, Ch. Rajendra Prasad, and Dasari Sindhu Sri
IEEE
Agriculture is regarded as one of the most crucial occupation in India and the backbone of the country's economy, because agriculture employs and sustains 70% of the Indian people. Soil quality and climatic conditions are two significant elements that influence agriculture. Choosing a crop that does not suit the soil or climatic conditions not only reduces the crop's quality but also its quantity. To address this issue, this research study has developed a system to evaluate the soil quality and also provide crop recommendations. It also anticipates the fertilizer needed for the crop and even the market place using machine learning algorithms to maintain the suggestions as precise as possible based on soil characteristics like as soil nutrients, moisture, and rainfall, and then deploying of the proposed model.
Srinivas Samala, Nakka Bhavith, Raghav Bang, Durshanapally Kondal Rao, Ch. Rajendra Prasad, and Srikanth Yalabaka
IEEE
Tomatoes are the most widely grown vegetable, used in a wide variety of dishes around the world. After potatoes and sweet potatoes, it is the third most extensively cultivated crop in the world. However, due to several diseases, both the quality and quantity of tomato harvests dedine. To maximize tomato yields, it is important to identify and eradicate the many diseases that harm the crop as early as possible. In this paper, we investigate the potential of deep learning techniques for diagnosing diseases on tomato leaves. The use of automatic methods for tomato leaf disease detection is helpful because it reduces the amount of monitoring needed in large-scale crop farms and does so at a very early stage when the signs of the disease identified on plant leaves are still easy to cure. The Kaggle dataset for tomato leaf disease was used for the study. A technique based on convolutional neural networks is used for disease identification and classification. Deep learning models, such as Inception V3 are used in this work. This proposed system obtained an accuracy of 99.60% suggesting that the neural network approach is effective even under difficult situations.
Ramyateja Singamshetty, Sangani Sruthi, Kodati Chandhana, Sreedhar Kollem, and Ch Rajendra Prasad
IEEE
This article presents a modern brain tumor recognition method heavily based on deep neural network techniques. Although huge datasets are challenging to train, deep convolutional neural networks have excelled in many computer vision tasks. The deadliest disease in humans, according to some estimates, is a brain tumor. Anormal cells in the body can or may not spread rapidly depending on the location of the tumor and its predicted growth rate. The dataset consists of brain tumor images. To enhance the performance of the image, a data augmentation technique is used to avoid overfitting issues. Overfitting is the process by which a network learns a function with a very high variance in order to perfectly model the training data. Access to big data is limited in many application domains, including medical image analysis. The main focus of this work is data augmentation, a data-space solution to the problem of limited data. Any techniques that increase the size of the dataset can be loosely referred to as data augmentation. For instance, the image can zoom in or out and save the result, alter the brightness of the image, or rotate it. The obtained data are trained with k-fold, a data partitioning technique that enables making the most of the dataset to create a more comprehensive model. Finally, an inception framework facilitates the ability to train much deeper networks than those previously used. Three image augmentation algorithms are covered in this article: data augmentation, the k-fold model, and the inception v3 model that classifies the images by minimizing overfitting issues. Lastly, there is an application page that contains a code in the backend that predicts whether a person is suffering from a brain tumor or not. Specificity, sensitivity, and accuracy are used to assess the performance of the suggested model.
Akula Shravya Sri, Bobbili Varshith Reddy, Kanuri Balakrishna, Vollala Akshitha, Sreedhar Kollem, and Ch Rajendra Prasad
IEEE
This article presents Multi-modal MRI scans used to classify brain tumors according to their size and imaging appearance. Object detection has been significantly improved by utilizing convolutional neural networks and deep learning approaches, resulting in superior performance. Our solution to tackle uncertainty involves a new deep learning method that incorporates pre-processing techniques such as data augmentation, as well as a customized convolutional neural network. The proposed method aims to achieve three objectives. (1) To address overfitting concerns, pre-processing techniques such as data augmentation are employed. (2) A customized deep learning method is used, which includes a convolutional neural network, to classify brain tumors. (3) A Web APP is utilized to provide information related to the tumor. The suggested procedure's performance is evaluated using sensitivity, specificity, and accuracy, and it outperforms traditional deep learning methods. Additionally, the entire process can be completed on the Python platform.
Ch.Rajendra Prasad, Ramya Bandi, Devulapally Aashrith, Anjali Sampelly, Maraboina Sai Chand, and Sreedhar Kollem
IEEE
Human Activities Recognition is the process of automatically identifying a person’s physical activities in order to create a secure environment for everyone, even elderly people, in their daily lives. In this paper, the classification of human activities using Conventional Neural networks with Principal Component Analysis with presented. In the proposed method, Principal Component Analysis is employed for dimensionality reduction and Conventional Neural networks are employed for classification. The Human Activities Recognition dataset from Kaggle is used in the suggested model. The effectiveness of the proposed model is assessed in terms of accuracy. The proposed model achieved an accuracy of about 96.71%.
Ch.Rajendra Prasad, Banothu Arun, Soma Amulya, Preethi Abboju, Sreedhar Kollem, and Srikanth Yalabaka
IEEE
Breast cancer is the deadliest and most common cancer in the world. Early treatment of this cancer can help to nip it in the bud. In present medical setting, this cancer is identified by manual clinical procedures, which can lead to human errors and further delay the treatment procedure. So, we propose a Convolutional Neural Network (CNN) model employed with transfer learning approach with RESNET50, VGG19 and InceptionV3 algorithms. The histopathological image dataset is used to detect cancer cells in the tissues of the breast. We examine the performance of different models based on their accuracy, by varying different optimizers (Adam, SGDM and RMSProp) for each transfer learning model. The results show that the Inception-V3 model with Adam optimizer outperforms VGG19 and RESNET-50 in terms of accuracy.
Ch. Rajendra Prasad, Polaiah Bojja, Sreedhar Kollem, and P. Ramchandar Rao
Springer Nature Singapore
Sreedhar Kollem, Katta Ramalinga Reddy, Duggirala Srinivasa Rao, Chintha Rajendra Prasad, V. Malathy, J. Ajayan, and Deboraj Muchahary
Wiley
Various image denoising algorithms have been developed for medical imaging. But some disadvantages have been found, including the block effect, which increases smoothing, and the loss of image detail. Using the statistical properties of observed noisy images, we propose a new diffusivity function‐based partial differential equation method for image denoising. This model incorporates a Quaternion Wavelet Transform for the generation of the various noisy image coefficients, an improved generalized cross‐validation function for generating the optimal threshold value via the soft threshold function, and a new diffusivity function for controlling the diffusion process. The fourth‐order PDE diffusivity function, which is presented in this article, is a novel diffusion coefficient that is more effective at removing noise and preserving edges than previous approaches. Finally, the performance of the proposed method is evaluated using the peak signal‐to‐noise ratio, mean square error, structural similarity index, and comparisons to other traditional methods.
P. Ramchandar Rao, Ch. Rajendra Prasad, Srinivas Samala, Sridevi Chitti, and Shyamsunder Merugu
AIP Publishing
V. Malathy, Sreedhar Kollem, Ch. Rajendra Prasad, and M. Anand
AIP Publishing
Y. Srikanth, Ch. Rajendra Prasad, P. Ramchandar Rao, and G. Sunil
AIP Publishing
Ch. Rajendra Prasad, P. Ramchandar Rao, Y. Srikanth, and A. Chakradhar
AIP Publishing
Syeda Bushra Shahewaz and Ch. Rajendra Prasad
AIP Publishing
The major problem, nowadays is the novel coronavirus or covid-19 pandemic which has and still infecting people and causing the death of many around the world. Although strict and severe actions have been taken by the government of all over the world to control and minimize the spread of this virus by lockdown, suspending, all travel and sport activity, economic and social activity, quarantines, travel restriction at stations and airport, etc, many of the people around the world have lost their lives and many are suffering. Recently, a conducted study has reported that in China the number of cases that were not documented is 79% as they show no symptom of the virus. In many other countries, the real representation of the patient infected by the virus was different from the real number which was higher in number. Hence, patients with asymptomatic conditions are the major reason for the fast and large covid-19 spread and are also the major reason that caused the people too afraid of the situation. To contribute this global pandemic, in this paper, we propose an investigation which is IoT based system designed to detect both symptomatic-asymptomatic infected people, undocumented, and the place of the pandemic. The aim is to make the authority and people aware of the situation by providing the total numbers of the cases registered, totally recovered, and death of the people caused by the pandemic. In the proposed system, we have used the IoT (internet of things) technology with the NODEMCU-ESP8266, LCD, and Wi- Fi connection to provide information of the important numbers of the patients of covid: cases, recovery, and death of the people in India. This system may keep people more informative, protected, and aware of the danger of the virus so they can take care of themselves and their families in every possible way. © 2022 Author(s).
RECENT SCHOLAR PUBLICATIONS
MOST CITED SCHOLAR PUBLICATIONS
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