Yalabaka Srikanth

@sru.edu.in

ASSISTANT PROFESSOR DEPARTMENT OF ECE
SR UNIVERSITY



                    

https://researchid.co/srikanthyalabaka

RESEARCH, TEACHING, or OTHER INTERESTS

Engineering, Electrical and Electronic Engineering

37

Scopus Publications

187

Scholar Citations

9

Scholar h-index

7

Scholar i10-index

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


  • Detection of motorbike rider without helmet and license plate recognition using convolutional neural network
    V. Malathy, N. Shilpa, S. M. Kamali, M. Anand, S. Vimala, G. Shiva, and Y. Srikanth
    AIP Publishing

  • Detection of glaucoma in retinal image using support vector machine
    N. Shilpa, V. Malathy, S. M. Kamali, M. Anand, S. Vimala, G. Shiva, and Y. Srikanth
    AIP Publishing

  • 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

  • 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

  • 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.

  • 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.

  • 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.

  • Brain Tumor Detection using modified VGG-19 and Inception ResnetV2 models
    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.

  • Multiclass MRI Brain Tumour Classification with Deep Transfer Learning
    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.

  • Kidney Cancer Detection using Deep Learning Models
    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.

  • AGRI-PRO: Crop, Fertilizer and Market Place Recommender for Farmers Using Machine Learning Algorithms
    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.

  • Disease Identification in Tomato Leaves Using Inception V3 Convolutional Neural Networks
    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.

  • Breast Cancer Classification using CNN with Transfer Learning Models
    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.

  • Detection of manhole status and alert system using at MEGA 328
    Y. Srikanth, Ch. Rajendra Prasad, P. Ramchandar Rao, and G. Sunil
    AIP Publishing

  • Review on energy harvesting techniques for wearable devices in wireless body area networks
    Ch. Rajendra Prasad, P. Ramchandar Rao, Y. Srikanth, and A. Chakradhar
    AIP Publishing

  • COVID - 19 social distance detector using at mega 328
    Y. Srikanth, Ch. Rajendra Prasad, P. Ramchandar Rao, and G. Sunil
    AIP Publishing

  • Smart exterior home management system using Arduino Uno and Tinkercad
    Ch. Rajendra Prasad, Y. Srikanth, P. Ramchandar Rao, and Kollem Sreedhar
    AIP Publishing

  • Devanagari Script Digit Classification using modified AlexNet with Transfer Learning
    Ch. Rajendra Prasad, Sreedhar Kollem, Srinivas Samala, P. Ramchandar Rao, Srikanth Yalabaka, and A. Chakradhar
    IEEE
    Digitization of ancient scripts is more important to make available for future generations. Devanagari script is the most popular ancient Indian literature of India. Over the last decade the researcher focusing on Devanagari script digit classification. This paper presents Devanagari script digit classification using modified AlexNet with transfer learning. The pre-trained AlexNet 23rd and 25th layers modified for the requirement of Devanagari script digit data. The proposed model employs a dataset from the Kaggle and Stochastic Gradient Descent with a momentum optimizer employed for training. The proposed model shows impressive results in terms of accuracy of 93.64%.

  • Handwritten Telugu Vowel Character Classification Using Modified 25-Layer AlexNET With Transfer Learning
    Srinivas Samala, Ch. Rajendra Prasad, Sreedhar Kollem, P. Ramchandar Rao, Srikanth Yalabaka, and Ramu Moola
    IEEE
    Due to remarkable performance, deep neural networks have gained popularity in computer vision and machine learning applications such as regression, segmentation, classification, detection, and pattern recognition over the past few years. This paper examines the categorization of handwritten telugu vowel characters using a modified 25-layer AlexNET and transfer learning approach. The six-class Handwritten Telugu vowel dataset from Kaggle is utilized in this experiment. To accommodate the required amount of vowel dataset classes, the 23rd and 25th layers of pre-trained AlexNET are adjusted. Training makes advantage of stochastic gradient descent using momentum optimizer. The proposed model accurately classified handwritten telugu vowel characters with a remarkable 99.72 % accuracy.

  • Lane Detection using Deep Learning Techniques
    Srikanth Yalabaka, Ch. Rajendra Prasad, P. Sanjana, B. Lokesh, T. Shradha, and Srinivas Samala
    IEEE
    Deep learning techniques provide solutions to various real-world problems and have given rise to many innovations. One such innovation is autonomous vehicles; the key parameter of autonomous vehicles is providing the correct path which is nothing but detecting the road lanes. We primarily focused on detecting the path using an image dataset. The dataset consists of images of roads taken during morning, night and in various weather conditions. The dataset contains around 5000+ images. The deep learning algorithm that we used is a convolution neural network. Convolution neural network mimics the human brain and it is the most used algorithm for image processing. We built a CNN model that takes an image as input and processes it through different layers and extracts the necessary features. The features extracted are used for identifying the path from the testing images with utmost accuracy. The deep learning model that can effectively detect the path is built using google colab.

RECENT SCHOLAR PUBLICATIONS

  • Intelligent Diagnosis Support System for Screening Diabetes Subjects using Hybrid Machine Learning Algorithms
    CR Prasad, S Samala, S Kollem, R Janapati, S Yalabaka, M Ramu
    Decision Support System for Diabetes Healthcare: Advancements and 2025

  • EfficientNetB3-DTL: Classification of Diabetic Retinopathy Images using Modified EfficientNetB3 with Deep Transfer Learning
    CR Prasad, S Kollem, S Samala, B Srinivas, R Janapati, S Yalabaka
    Decision Support System for Diabetes Healthcare: Advancements and 2025

  • 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

  • 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 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

  • 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

  • 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

  • Detection of glaucoma in retinal image using support vector machine
    N Shilpa, V Malathy, SM Kamali, M Anand, S Vimala, G Shiva, Y Srikanth
    AIP Conference Proceedings 2971 (1) 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

  • Detection of motorbike rider without helmet and license plate recognition using convolutional neural network
    V Malathy, N Shilpa, SM Kamali, M Anand, S Vimala, G Shiva, Y Srikanth
    American Institute of Physics Conference Series 2971 (1), 030012 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

  • Comprehensive CNN model for brain tumour identification and classification using MRI images
    CR Prasad, K Srividya, K Jahnavi, T Srivarsha, S Kollem, S Yelabaka
    2024 IEEE International Conference for Women in Innovation, Technology 2024

  • Brain Tumor Detection using modified VGG-19 and Inception ResnetV2 models
    CR Prasad, S Hussain, B Srinivas, S Samala, R Janapati, S Yalabaka
    2023 IEEE 2nd International Conference on Industrial Electronics 2023

  • Multiclass MRI Brain Tumour Classification with Deep Transfer Learning
    CR Prasad, S Mohammed, PR Rao, S Kollem, S Samala, S Yalabaka
    2023 3rd Asian Conference on Innovation in Technology (ASIANCON), 1-4 2023

  • Disease Identification in Tomato Leaves Using Inception V3 Convolutional Neural Networks
    S Samala, N Bhavith, R Bang, DK Rao, CR Prasad, S Yalabaka
    2023 7th International Conference on Trends in Electronics and Informatics 2023

  • 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

  • AGRI-PRO: Crop, Fertilizer and Market Place Recommender for Farmers Using Machine Learning Algorithms
    Y Srikanth, M Daddanala, M Sushrith, P Akula, CR Prasad, DS Sri
    2023 7th International Conference on Trends in Electronics and Informatics 2023

MOST CITED SCHOLAR PUBLICATIONS

  • 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: 20

  • 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: 17

  • 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: 15

  • Security enhancement of genome sequence data in health care cloud
    G Sunil, S Aluvala, N Yamsani, KR Chythanya, S Yalabaka
    International Journal of Advanced Trends in Computer Science and Engineering 2019
    Citations: 13

  • Customer segmentation using machine learning
    N Gankidi, S Gundu, M viqar Ahmed, T Tanzeela, CR Prasad, S Yalabaka
    2022 2nd International Conference on Intelligent Technologies (CONIT), 1-5 2022
    Citations: 12

  • Lane Detection using Deep Learning Techniques
    S Yalabaka, CR Prasad, P Sanjana, B Lokesh, T Shradha, S Samala
    2022 8th International Conference on Signal Processing and Communication 2022
    Citations: 10

  • An IOT based environmental monitoring system
    GJ Rani, GS Rama, RK Marrikukkala, Y Srikanth, CVK Reddy
    IOP Conference Series: Materials Science and Engineering 981 (3), 032025 2020
    Citations: 10

  • Disease Identification in Tomato Leaves Using Inception V3 Convolutional Neural Networks
    S Samala, N Bhavith, R Bang, DK Rao, CR Prasad, S Yalabaka
    2023 7th International Conference on Trends in Electronics and Informatics 2023
    Citations: 9

  • Optimization of task offloading for smart cities using IoT with fog computing-a survey
    CR Prasad, S Kumar, PR Rao, S Kollem, S Yalabaka, S Samala
    2022 International Conference on Signal and Information Processing (IConSIP 2022
    Citations: 9

  • Facial emotional recognition system using machine learning
    S Tanveez, MD Amer, C Vamshika, AS Reddy, CR Prasad, S Yalabaka
    2022 2nd International Conference on Intelligent Technologies (CONIT), 1-4 2022
    Citations: 9

  • Brain Tumor Detection using modified VGG-19 and Inception ResnetV2 models
    CR Prasad, S Hussain, B Srinivas, S Samala, R Janapati, S Yalabaka
    2023 IEEE 2nd International Conference on Industrial Electronics 2023
    Citations: 7

  • Review on energy harvesting techniques for wearable devices in wireless body area networks
    CR Prasad, PR Rao, Y Srikanth, A Chakradhar
    AIP conference proceedings 2418 (1) 2022
    Citations: 7

  • Multiclass MRI Brain Tumour Classification with Deep Transfer Learning
    CR Prasad, S Mohammed, PR Rao, S Kollem, S Samala, S Yalabaka
    2023 3rd Asian Conference on Innovation in Technology (ASIANCON), 1-4 2023
    Citations: 6

  • Handwritten Telugu vowel character classification using modified 25-layer alexnet with transfer learning
    S Samala, CR Prasad, S Kollem, PR Rao, S Yalabaka, R Moola
    2022 international conference on smart generation computing, communication 2022
    Citations: 6

  • Digital error correction logic for pipelined ADC using 1.5 bits/stage
    Y Srikanth, CR Prasad, KR Danthamala, PR Rao, A Chakradhar
    IOP conference series: materials science and engineering 981 (3), 032046 2020
    Citations: 6

  • Devanagari Script Digit Classification using modified AlexNet with Transfer Learning
    CR Prasad, S Kollem, S Samala, PR Rao, S Yalabaka, A Chakradhar
    2022 International Conference on Smart Generation Computing, Communication 2022
    Citations: 5

  • Area-power analysis of carry select adder using transmission gates
    Y Srikanth, CR Prasad, PR Rao, GJ Rani, A Chakradhar
    IOP Conference Series: Materials Science and Engineering 981 (3), 032045 2020
    Citations: 5

  • Pentagon shaped microstrip antenna for wireless iot applications
    PS Sundar, SK Kotamraju, SKK Ch, B Madhav, Y Srikanth, N Babu
    Journal of Critical Reviews 7 (14), 539-543 2020
    Citations: 4

  • AGRI-PRO: Crop, Fertilizer and Market Place Recommender for Farmers Using Machine Learning Algorithms
    Y Srikanth, M Daddanala, M Sushrith, P Akula, CR Prasad, DS Sri
    2023 7th International Conference on Trends in Electronics and Informatics 2023
    Citations: 3

  • Smart exterior home management system using Arduino Uno and Tinkercad
    CR Prasad, Y Srikanth, PR Rao, K Sreedhar
    AIP Conference Proceedings 2418 (1) 2022
    Citations: 3