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
Y. Srikanth, Ch. Rajendra Prasad, S. Srinvas, Sreedhar Kollem, and Rajesh Thota
AIP Publishing
Ch. Rajendra Prasad, Srikanth Yalabaka, Sreedhar Kollem, Srinivas Samala, and P. Ramchandar Rao
AIP Publishing
Srinivas Samala, Ch. Rajendra Prasad, Sreedhar Kollem, Srikanth Yalabaka, and P. Ramchandar Rao
AIP Publishing
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
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.
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.
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.
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.
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%.
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.
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.
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%.
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.
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.
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.
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.
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.
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