Venkatramaphanikumar Sistla

Scopus Publications

Enhancing plant leaf disease classification through self-attention super-resolution GAN and dual attention model
V. Sasikala, Venkatramaphanikumar Sistla, Deepak Chowdary Edara, Venkata Krishna Kishore Kolli
Visual Computer, 2026
Sarcasm detection using optimized bi-directional long short-term memory
Vidyullatha Sukhavasi, Venkatrama Phani kumar Sistla, Venkatesulu Dondeti
Knowledge and Information Systems, 2025
A Cascaded Ensemble Framework Using BERT and Graph Features for Emotion Detection From English Poetry
Praveen K. Kazipeta, Venkatrama Phani Kumar Sistla, Venkata Krishna Kishore Kolli
IEEE Access, 2025
Researchers have developed complex deep-learning models to extract emotions from poetry, opting for these over lightweight models. However, this approach requires a high volume of resources, which can be a significant limitation. Moreover, they often suffer from overfitting, making them less effective in real-time scenarios. This work introduces a novel cascaded ensemble framework that combines the strengths of BERT and Graph features (CP Net). The framework is designed in a tiered approach, classifying the majority of emotions in poetry, thereby reducing the need to invoke more complex models later on. This strategic arrangement enables efficient resource allocation and minimizes the usage of complex models. The basic model may not classify all so that a later model will classify residual unclassified emotions in the poetry. While the basic models in the first phase classify the majority of emotions in poetry, the remaining unclassified emotions are then passed on to the subsequent stages of the model, where more complex models can further process and classify them. This work performs feature fusion only in case of failed input samples. The performance of the proposed model is evaluated and compared against four baseline models using word embedding models, including Glove and Fast Text. The proposed CP Net model demonstrated exceptional performance, surpassing all other models by achieving an impressive 95% accuracy on the CAPEMO dataset and an outstanding 98% accuracy on the BAPEMO dataset. CP Net achieved state-of-the-art results with a minimum computational time of 0.274 to 0.332 milliseconds, outperforming other models.
Optimised feature selection and categorisation of medical records with multi kernel boosted support vector machine
V. Lakshmi Prasanna, E. Deepak Chowdary, S. Venkatramaphanikumar, K. Venkata Krishna Kishore
International Journal of Advanced Intelligence Paradigms, 2025
With the fast growth of internet and mobile usage, huge volumes of medical documents, which contain information of patients, diagnostic, past disease history and medication, are being generated electronically. In the field of text mining, document categorisation has become one of the emerging techniques due to large volume of documents in the form of digital data. The main objective of the proposed work is to identify disease treatment relationships and predict the diseases among medical articles. In this paper, highly relevant and more correlated features have been extracted using probabilistic latent Dirichlet allocation (P-LDA) and randomised iterative feature selection approach. These features were classified with multi kernel boosted support vector machine (MKB-SVM) and then their performance was evaluated on both PubMed and MEDLINE databases. Performance evaluation of the proposed approach on DB-1 and DB-2 was 98.7% and 92%, respectively. The evaluation illustrated that the proposed approach outperformed the existing state-of-the-art classification methods.
A Synergistic Stacked Ensemble Deep Learning Model for Predicting Diabetic Retinopathy Severity
Deva Kumar Salluri, Maanas Sai Surya Chandra Atthuluri, Venkatramaphanikumar Sistla, Venkata Krishna Kishore Kolli
IEEE Access, 2025
Diabetic retinopathy is a leading cause of vision loss worldwide. Early and accurate diagnosis can prevent vision loss, and automatic grading is particularly important to assist physicians in making timely decisions. This work proposes an efficient stacked ensemble framework for the DR severity classification task. Unlike traditional ensembles, our model leverages out-of-fold predictions and collaborative meta-learning to improve ordinal consistency without leaking information during training. We incorporate the best features of three top deep learning architectures: Swin-B Transformer, EfficientNetV2-L, and ConvNeXt-L. A meta-learner maximizes their collective capacity for prediction. Extensive experiments on two widely available datasets, the APTOS 2019 Blindness Detection dataset and a larger Kaggle dataset, were conducted to evaluate our approach. We investigated the generalization capability of our ensemble with both 3-fold and 5-fold cross-validation. With the highest classification accuracy of 97.9% for the APTOS dataset and 98.2% for the Kaggle dataset, the results clearly indicate that our stacked model outperforms all individual networks. Furthermore, for both datasets, we consistently achieved a high QWK value of 0.97. This reflects strong agreement between our predictions and professional clinical evaluations. It also demonstrates the goodness of our model dealing with ordinal data. These findings emphasize that there is great potential in Stacked Ensembling to enhance the accuracy and robustness of automated diabetic retinopathy grading systems, particularly when combined with diverse high performance base models.
FedViT: A Privacy-Aware Federated Vision Transformer for Diabetic Retinopathy Detection
A. Maanas Sai Surya Chandra, Deva Kumar Salluri, Venkatramaphanikumar Sistla, Venkata Krishna Kishore Kolli
Iranian Journal of Science and Technology Transactions of Electrical Engineering, 2025
An Improved Inception V3 Deep learning model for Cardiovascular Disease Prediction
Lingineni Lakshmi Sowjanya, Kodali Kowshya, Manduri Sai Roshini, Bogireddy Krishna, Venkatrama Phani Kumar S, Venkata Krishna Kishore Kolli
Esic 2025 5th International Conference on Emerging Systems and Intelligent Computing Proceedings, 2025
Accurate prediction of cardiovascular disease risk is essential for improving healthcare outcomes and resource management. This study uses a cardiovascular disease dataset and applies deep learning models to forecast disease risk in specific patient populations. In our approach, data preprocessing encompassed normalization and partitioning of the dataset into training and test subsets. We utilized the Inception V3 model, conducting training over ten epochs for data analysis. We also assessed alternative models to broaden our evaluation—specifically, VGG 16, VGG 19, ResNet 34, and ResNet 50. These models were analyzed based on essential performance metrics, such as accuracy, precision, recall, and F1 score. Inception V3 achieved the highest accuracy at 98%, followed by VGG 16 at 86%, VGG 19 at 80%, ResNet 34 at 78%, and ResNet 50 at 68%. We further enhanced our analysis with visual tools such as histograms and heat maps to identify key trends and correlations in the data. These insights provided a clearer understanding of significant risk factors and potential prevention strategies. Our findings demonstrate that deep learning models, particularly Inception V3, are highly effective for cardiovascular disease prediction, contributing to improved healthcare strategies and decision-making.
Performance Evaluation of Various Deep Learning Models for Sign Language Recognition
Vanipenta Sai Ganesh Reddy, Bollimuntha AnkammaRao, Chilakapati Manvitha, Kurapati Priyanka, Venkatrama Phani Kumar Sistla, Venkata Krishna Kishore Kolli
Esic 2025 5th International Conference on Emerging Systems and Intelligent Computing Proceedings, 2025
This study investigates the application of deep learning for automatic recognition of American Sign Language (ASL) alphabets and numbers (0-10). Utilizing a Kaggle dataset comprising 2,520 images representing 36 unique ASL signs, we processed the images by resizing them to 64 x 64 pixels and dividing them into training and testing subsets to maintain balanced learning. We assessed the performance of various convolutional neural network (CNN) architectures, including a basic CNN, VGG-16, and ResNet-50, for feature extraction and classification. The models were evaluated using essential performance metrics accuracy, precision, recall, and F1-score to ensure their robustness and reliability. Among these models, ResNet-50 achieved the highest performance with an accuracy of 94.05%. This research highlights the potential of deep learning to facilitate communication between the Deaf and hard-of-hearing communities and the general public through rapid, automated ASL recognition, contributing to a more inclusive communication solution.
A Bagging Based Machine Learning Model for the Prediction of Dietary Preferences
Harshitha Kotapati, Teja Annamdevula, Yeswanth Tavva, Sai Sahitya Chennam, Sistla Venkatrama Phani Kumar, Kolli Venkata Krishna Kishore
Lecture Notes in Networks and Systems, 2025
Automated Kidney Anomaly Detection Using Deep Learning and Explainable AI Techniques
Bobba Siva Sankar Reddy, Nelluru Laxmi Prathyusha, Dhulipudi Venkata Karthik, Kayala Vishnukanth, Venkatrama Phani Kumar Sistla, Venkata Krishna Kishore Kolli
2025 International Conference on Pervasive Computational Technologies Icpct 2025, 2025
Accurate diagnosis of kidney abnormalities such as cysts and tumors is essential for timely and effective treatment. This paper describes an in-depth study on recognizing and identifying kidney abnormalities using CT imaging. Convolutional neural sneŧworks are popular due to their excellent ability to extract complex features from large amounts of medical data and achieve better results than conventional methods. The proposed method combines CNN and previous learning models, including VGG16, EfficientNetB0, and MobileNetV2, all optimized to improve performance and robustness across multiple kidney types. The model shows good specificity in classifying some cases, helping to make more reliable diagnoses. Additionally, descriptive artificial intelligence (XAI) techniques such as LIME and SHAP are used to improve the model by identifying key features that affect the cut-off. This helps increase radiologists’ confidence, improve interpretation, and facilitate clinical decision-making. The results show that this model has the potential to help improve early diagnosis, ultimately improving patient outcomes and diagnostic procedures.
Deep Learning Based Real Time Semantic Segmentation of Autonomous Vehicles
Himaja Paladugu, Venkatrama Phani Kumar Sistla, Gadde Vineela, Nagalakshmi Anusha Kukkapalli, Maddi Hruthik, Venkata Krishna Kishore Kolli
IEEE International Conference on Computational Communication and Information Technology Icccit 2025, 2025
An Investigative Comparison of Various Deep Learning Models for Driver Drowsiness Detection
Sai Ganesh Nannapaneni, Umesh Reddy Arimanda, Raghavendra Sai Boddu, Venkata Siddardha Mogalluri, Venkatrama Phani Kumar Sistla, Venkata Krishna Kishore Kolli
International Conference on Intelligent Systems and Computational Networks Iciscn 2025, 2025
Natural Disaster Prediction Using Deep Learning
Guntaka Mahesh Vardhan Reddy, Pasupulati BharatwajTeja, Kommalapati Thirumala Devi, Karumuri Rahul Dev, Deva Kumar S, Venkatrama Phani Kumar Sistla
IEEE International Conference on Computational Communication and Information Technology Icccit 2025, 2025
An Experimental Study of Binary Classification on Imbalanced Datasets
Dhanasiri Vamsi Krishna, Varada Kavya, Chirumamilla Sneha, Machavarapu Sai Sravya Sri, S. Venkatrama Phani Kumar, et al.
Lecture Notes in Networks and Systems, 2025
Comparative Study of Different Pre-Trained Deep Learning Models for Footwear Classification
Chandu Boppana, Naga Amrutha Chituri, Vamsi Pallapu, Bala Vamsi Boyapati, Venkatrama Phani Kumar, Venkata Krishna Kishore Kolli
International Conference on Intelligent Systems and Computational Networks Iciscn 2025, 2025
Experimental Study of Various Deep Learning Models in Handwritten Character Recognition
Prudhvi Chanakya Nalluri, Siva Rama Sandilya Ponnekanti, Navya Annapureddy, Mupparaju Vijaya Lakshmi, Venkatrama Phani Kumar Sistla, Venkata Krishna Kishore Kolli
2025 5th International Conference on Advances in Electrical Computing Communication and Sustainable Technologies Icaect 2025, 2025
A Novel Deep Learning Model for Machine Fault Diagnosis
K Geethika, V Sowmya, K Ravi Kiran, K Neelaveni, Venkatrama Phani Kumar, Venkata Krishna Kishore Kolli
2025 International Conference on Pervasive Computational Technologies Icpct 2025, 2025
Bi-GRU and Glove Based Aspect-Level Movie Recommendation
Achanta Haritha, Kalagara Joshanth, Venkatrama Phani Kumar Sistla, Vangipurapu Veera Brahma Chaitanya, Chilukuri Vijay Rami Reddy, Venkata Krishna Kishore Kolli
IEEE International Conference on Computational Communication and Information Technology Icccit 2025, 2025
Image Enhancement and De-blurring using ResNet-101
Kusuma Gunduboina, Mohammad Nayeemul Haq, Chennamsetty Sasidhar, Kakani Prudhvi, Venkatrama Phani Kumar Sistla, Venkata Krishna Kishore Kolli
Esic 2025 5th International Conference on Emerging Systems and Intelligent Computing Proceedings, 2025
An Experimental Study on Renewable Energy Forecasting and Prediction Using Deep Learning
Sai Sahitya Ch, R Sai Keerthana, V John Lily Joel, Sk Samiyan, S Sirisha, S Venkatrama Phani Kumar
6th IEEE International Conference on Recent Advances in Information Technology Rait 2025, 2025
An Experimental Study on Prediction of Lung Cancer from CT Scan Images
Abhishek Mandala, Venkata Seetha Ramanjaneyulu Kurapati, Siva Rama Krishna Musunuri, J V S Choudhari Mutthina, Venkatrama Phani Kumar Sistla, Venkata Krishna Kishore Kolli
International Conference on Intelligent Systems and Computational Networks Iciscn 2025, 2025
A Novel Transfer Learning-based Efficient-Net for Visual Image Tracking
Sowmya Sri Puligadda, Galla Karthik, Usha Polina, Sai Subbarao, Venkatrama Phani Kumar Sistla, Venkata Krishna Kishore Kolli
2025 International Conference on Pervasive Computational Technologies Icpct 2025, 2025
A Novel Deep Learning Model Based Lung Cancer Detection of Histopathological Images
Chirumamilla Sneha, Kanaparthi Satish Babu, Kureti Manikanta, Vemulapallii Manjunadha, S Deva Kumar, S Venkatrama Phani Kumar
IEEE International Conference on Computational Communication and Information Technology Icccit 2025, 2025
A novel hierarchical framework for plant leaf disease detection using residual vision transformer
Sasikala Vallabhajosyula, Venkatramaphanikumar Sistla, Venkata Krishna Kishore Kolli
Heliyon, 2024
DeepEmoNet: An Efficient Deep Emotional Neural Network for Facial Expression Recognition
Dulla Likhitha, Venkatrama Phani Kumar Sistla
Proceedings of the 3rd International Conference on Applied Artificial Intelligence and Computing Icaaic 2024, 2024
A systematic Study on the Usage of Various Attention Mechanisms for Plant Leaf Disease Detection
Sasikala Vallabhajosyula, Venkatramaphanikumar Sistla, Venkata Krishna Kishore Kolli
2024 Asian Conference on Intelligent Technologies Acoit 2024, 2024
An investigative study on the usage of various GAN models for Plant Leaf Disease Detection
Sasikala Vallabhajosyula, Venkatramaphanikumar Sistla, Venkata Krishna Kishore Kolli
Proceedings of the 5th International Conference on Smart Electronics and Communication Icosec 2024, 2024
LSTM-based statistical framework for human activity recognition using mobile sensor data
Y. Vijaya Durga, S. Venkatramaphanikumar, K.V. Krishna Kishore
International Journal of Advanced Intelligence Paradigms, 2024
Bi-LSTM Based Real-Time Human Activity Recognition From Smartphone Sensor Data
Sai Vyshnavi Modukuri, Neha Mogaparthi, Sahithi Burri, Ravi Kiran Kalangi, Venkatrama Phani Kumar S
Proceedings of the 2024 International Conference on Artificial Intelligence and Emerging Technology Global AI Summit 2024, 2024
Ant-BERT Based Optimized Fake News Detection
Shanmukha Sudha Kiran Thotakura, Sriram Budankayala, Amrutha Sri Chandana Pallapothu, Dinesh Kumar Kata, Venkatrama Phani Kumar S
Proceedings of the 2024 International Conference on Artificial Intelligence and Emerging Technology Global AI Summit 2024, 2024
An Experimental Analysis of Association Rule Mining Algorithms to Extract Strong and Interesting Association Rules
G. Vineela, M. Alekhya, A. Jayanth, J.M.V. Lakshmi Harshitha, Venkatrama Phani Kumar S, Venkata Krishna Kishore K
2nd IEEE International Conference on Advances in Information Technology Icait 2024 Proceedings, 2024
An Investigative Analysis on Generation of AI Text Using Deep Learning Models for Large Language Models
Saranyya Chimata, Ankamma Rao Bollimuntha, DevaKiran Devagiri, Sowmyasri Puligadda, Venkatrama Phani Kumar S, Venkata Krishna Kishore K
International Conference on Smart Systems for Electrical Electronics Communication and Computer Engineering Icsseec 2024 Proceedings, 2024
A Light Gradient Boosted Model for Network Intrusion Detection
Vyshnavi Manduru, Tharun Gopi Reddy Kasireddy, Karthik Manchina, Vijay Shanmuk Davuluri, Venkatrama Phani Kumar S, Venkata Krishna Kishore K
International Conference on Smart Systems for Electrical Electronics Communication and Computer Engineering Icsseec 2024 Proceedings, 2024
Prediction of Food Wastage using XG Boost
K. Sruthi Chowdary, L. Krishna Praneetha, S. Holika, D. Bindhu Priya, S Venkatrama Phani Kumar, K Venkata Krishna Kishore
Proceedings 2024 8th International Conference on Inventive Systems and Control Icisc 2024, 2024
An Experimental Study on Prediction of Video Ads Engagement
Tanuja Ch, Sai Sevitha V, Veera Manikanta V, Prabhath Chowdary G, Venkatarama Phanikumar S, Venkata Krishna Kishore K
2nd IEEE International Conference on Advances in Information Technology Icait 2024 Proceedings, 2024
An Ensemble Machine Learning Model to Predictive Analysis of End to End Uber Data
Sriramu Tanniru, Harika Tummala, Kowshya Kodali, Revanth Manyam, Mr.S Jaya Sankar, Venkatrama Phani Kumar S
2024 Asia Pacific Conference on Innovation in Technology Apcit 2024, 2024
An Efficient Seq2Seq model to predict Question and Answer response system
Pujitha Nerella, DivyaSri Pittu, Sandhya Undrakonda, Sasidhar Chennamsetty, Venkatrama Phani Kumar S, Venkata Krishna Kishore K
2nd IEEE International Conference on Advances in Information Technology Icait 2024 Proceedings, 2024
An Extra Tree Classifier for prediction of End to End Customer Churn and Retention
G. Gayathri, P Priyanka, B Manjeera, K Niharika, Venkatrama Phani Kumar S, Venkata Krishna Kishore K
2024 Asia Pacific Conference on Innovation in Technology Apcit 2024, 2024
Optimizing Music Genre Classification: A Hybrid Approach with ACO and Ensemble Learning
A Sai Kiran Reddy, V Likitha, P Venkata Durga Prasad, V Naga Bharath, S Venkatrama Phani Kumar, K Venkata Krishna Kishore
2024 Asian Conference on Intelligent Technologies Acoit 2024, 2024
Prediction of Food Ingredient Pairings using Siamese Neural Networks
Jahnavi Naga Sai Sighakolli, Viswanath Vangipurapu, Jai Rama Srinivas Nadella, Yashwanth Vennapu, S Deva Kumar, S Venkatarama Phani Kumar
Proceedings 2024 8th International Conference on Inventive Systems and Control Icisc 2024, 2024
Prediction of Credit Card Fraud detection using Extra Tree Classifier and Data Balancing Methods
Vinay Kumar Naramala, Lakshmi Prasanna Ravipudi, Surya Prakesh Reddy Bhavanam, Venkata Narayana Pokuri, Venkatrama Phani Kumar S, Venkata Krishna Kishore
2024 IEEE 3rd World Conference on Applied Intelligence and Computing Aic 2024, 2024
Sentiment Analysis Using CEmo-LSTM to Reveal the Emotions from Tweets
Annam Jayasri, Shaik Rafiya Nasreen, Atchuta Kavya, Sayyad Karimun, Venkatrama Phani Kumar S, Venkata Krishna Kishore K
2nd IEEE International Conference on Data Science and Network Security Icdsns 2024, 2024
An Experimental Study on Prediction of Revenue and Customer Segmentation
Bhavya Sai Mikkilineni, Uuhasri Madala, Renu Sree Bonthagorla, Yashmitha Priya Parikala, S Venkatrama Phani Kumar, Venkata Krishna KishoreK
Proceedings 2024 8th International Conference on Inventive Systems and Control Icisc 2024, 2024
Impact Analysis of Feature Selection in Supervised and Unsupervised Methods
Anusha Kukkapalli, Anuhya Kavuri, Venkata Sai Sudheer Kumar Batchu, Khaja Syed, Venkatrama Phani Kumar S, Venkata Krishna Kishore K
2024 IEEE 3rd World Conference on Applied Intelligence and Computing Aic 2024, 2024
A Novel Boosted Machine Learning Model for Software Defective Prediction
Satya Lakshmi G, Lakshmi Harshitha P, Krishna Babu Y, Venkata Ramya M, Venkatrama Phani Kumar S, Venkata Krishna Kishore K
2024 Asia Pacific Conference on Innovation in Technology Apcit 2024, 2024
Leveraging CAT Boost for Enhanced Prediction of App Ratings in the Google Play Store
Jayanth. P, Abhiram Nagam, Priyanka Undavalli, Priya. P, Venkatrama Phani Kumar S, Venkata Krishna Kishore K
2nd IEEE International Conference on Advances in Information Technology Icait 2024 Proceedings, 2024
Prediction of Solar Panel Maintenance using BiLSTM
Ajay Bhaskar Sanganaboina, Sandeep Ruttala, Hrushikesh Mandadapu, Sai Venkata Uma Maheswararao Kanigiri, S Deva Kumar, S Venkatrama Phani Kumar
Proceedings of the 3rd International Conference on Applied Artificial Intelligence and Computing Icaaic 2024, 2024
A Novel Machine Learning Model for Sarcasm Detection on Facebook Comments
Soumya Puvvada, Kusuma, Mastan Valli Shaik, Karthik Galla, S Venkatrama Phani Kumar, K Venkata Krishna Kishore
Proceedings of the 3rd International Conference on Applied Artificial Intelligence and Computing Icaaic 2024, 2024
An automated online proctoring system using attentive-net to assess student mischievous behavior
Tejaswi Potluri, Venkatramaphanikumar S, Venkata Krishna Kishore K
Multimedia Tools and Applications, 2023
Sentiment analysis and text categorization of cancer medical records with LSTM
Deepak Chowdary Edara, Lakshmi Prasanna Vanukuri, Venkatramaphanikumar Sistla, Venkata Krishna Kishore Kolli
Journal of Ambient Intelligence and Humanized Computing, 2023
DETECTING AUTISM OF EXAMINEE IN AUTOMATED ONLINE PROCTORING USING EYE-TRACKING
Journal of Theoretical and Applied Information Technology, 2023
MG-Net: Multiple Person and Gadget Detection for Online Exam Proctoring System
Tejaswi Potluri, Venkatrama Phani Kumar Sistla
Lecture Notes in Networks and Systems, 2023
Proctor net: An AI framework for suspicious activity detection in online proctored examinations
P. Tejaswi, S. Venkatramaphanikumar, K. Venkata Krishna Kishore
Measurement Journal of the International Measurement Confederation, 2023
HIUNET: A Hybrid Inception U-Net for Diagnosis of Diabetic Retinopathy
S. Deva Kumar, S. Venkatramaphanikumar, K. Venkata Krishna Kishore
Intelligent Automation and Soft Computing, 2023
HRUNET: Hybrid Residual U - Net for automatic severity prediction of Diabetic Retinopathy
Deva Kumar Salluri, Venkatramaphanikumar Sistla, Venkata Krishna Kishore Kolli
Computer Methods in Biomechanics and Biomedical Engineering Imaging and Visualization, 2023
Brain Tumor Segmentation Using 3D Attention U Net
Siva Koteswara Rao Chinnam, Venkatramaphanikumar Sistla, Venkata Krishna Kishore Kolli
Communications in Computer and Information Science, 2023
Multimodal attention-gated cascaded U-Net model for automatic brain tumor detection and segmentation
Siva Koteswara Rao Chinnam, Venkatramaphanikumar Sistla, Venkata Krishna Kishore Kolli
Biomedical Signal Processing and Control, 2022
Transfer learning-based deep ensemble neural network for plant leaf disease detection
Sasikala Vallabhajosyula, Venkatramaphanikumar Sistla, Venkata Krishna Kishore Kolli
Journal of Plant Diseases and Protection, 2022
Deep finesse network model with multichannel syntactic and contextual features for target-specific sentiment classification
Deepak Chowdary Edara, Venkatramaphanikumar Sistla, Venkata Krishna Kishore Kolli
Applied Intelligence, 2022
Sentiment analysis with word-based Urdu speech recognition
Riyaz Shaik, S. Venkatramaphanikumar
Journal of Ambient Intelligence and Humanized Computing, 2022
Prediction of Cardiovascular Risk Using Extreme Learning Machine-Tree Classifier on Apache Spark Cluster
Venkatramaphanikumar S., Jaya Lakshmi A, Venkata K. K. Kolli
Recent Advances in Computer Science and Communications, 2022
A Comprehensive Survey on the AI Based Fully Automated Online Proctoring Systems to detect Anomalous Behavior of the Examinee
Tejaswi Potluri, Venkatarama Phani Kumar Sistla
Proceedings 2022 International Conference on Recent Trends in Microelectronics Automation Computing and Communications Systems Icmacc 2022, 2022
Deep Refinement Network for Target based Aspect-level Sentiment Analysis on User Generated Content in Social Media
Deepak Chowdary E, Venkataramaphanikumar S, Venkata Krishna Kishore Kolli
2022 2nd International Conference on Intelligent Technologies Conit 2022, 2022
Deep Sentient network with multifarious features and inter-mutual attention mechanism for target-specific sentiment classification
Deepak Chowdary Edara, Venkataramaphanikumar S, Venkata Krishna Kishore Kolli
2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation Iatmsi 2022, 2022
Analysis of Indian and American poetry using topic modeling and Deep learning
K Praveen Kumar, Venkatrama Phani Kumar S, S.K. Lokesh Kumar
Materials Today Proceedings, 2022
Planet Adventures: An Augmented Reality Game using Facial Emotion Recognition
Aaliq Moin, Lohitha Kolli, Venkatramaphanikumar Sistla, Venkata Krishna Kishore Kolli
International Journal of Computing and Digital Systems, 2022
Smart farming - A flexible approach to improve crop yield and profit using machine learning techniques
G Sravan Kumar, S Venkatramaphanikumar, K Venkata Krishna Kishore
2021 2nd International Conference for Emerging Technology Incet 2021, 2021
Prediction of pneumonia from chest X-ray images using pre-trained convolutional neural networks
Venkata Krishna Kishore Kolli, Venkatramaphanikumar Sistla, Sasikala Vallabhajosyula
Lecture Notes on Data Engineering and Communications Technologies, 2021
Review on the usage of deep learning models in multi-modal sentiment analysis
Naga Durga Saile K, Venkatramaphanikumar S, Venkata Krishna Kishore K, Debnath Bhattacharyya
Ieie Transactions on Smart Processing and Computing, 2020
Object detection using stacked YOLOv3
Sai Shilpa Padmanabula, Ramya Chowdary Puvvada, Venkatramaphanikumar Sistla, Venkata Krishna Kishore Kolli
Ingenierie Des Systemes D Information, 2020
Stacked ensemble classification based real-time driver drowsiness detection
Venkatramaphanikumar Sistla, Venkata Kolli, Navya Kukkapalli, Sri Katuri, Sasikala Vallabhajosyula
International Journal of Safety and Security Engineering, 2020
Predictive model for network intrusion detection system using deep learning
Venkatramaphanikumar Sstla, Venkata Kolli, Lakshmi Voggu
Revue D Intelligence Artificielle, 2020
Performance evaluation of generative adversarial networks for computer vision applications
Sravani Nannapaneni, Venkatramaphanikumar Sistla, Venkata Krishna Kishore Kolli
Ingenierie Des Systemes D Information, 2020
Health App Recommendation System using Ensemble Multimodel Deep Learning
Deepak Chowdary Edara, , Venkatramaphanikumar Sistla, Venkata Krishna Kishore Kolli
Journal of Engineering Science and Technology Review, 2020
Optimized Water Scheduling Using IoT Sensor Data in Smart Farming
Kolli Venkatra Krishna Kishore, B. Yaswanth Kumar, S. Venkatramaphanikumar
Lecture Notes in Networks and Systems, 2020
Aspect-level sentiment analysis on goods and services tax tweets with dropout DNN
E. Deepak Chowdary, S. Venkatramaphanikumar, K. Venkata Krishna Kishore
International Journal of Business Information Systems, 2020
A novel adaptive two-phase multimodal biometric recognition system
International Arab Journal of Information Technology, 2019
SVM-PUK kernel based MRI-brain tumor identification using texture and Gabor wavelets
Siva Chinnam, Venkatramaphanikumar Sistla, Venkata Kolli
Traitement Du Signal, 2019
Scale invariant face recognition with gabor wavelets and SVM
International Journal of Recent Technology and Engineering, 2019
Automatic speaker recognition system in urdu using MFCC & HMM
International Journal of Recent Technology and Engineering, 2019
A novel energy efficient scheduling for VM consolidation and migration in cloud data centers
Dasari Yakobu, Chirra Reddy, Venkatramaphani Sistla
Ingenierie Des Systemes D Information, 2019
A hybrid framework for detection of diseases in apple and tomato crops with deep feed forward neural network
R. Praneetha, S. Venkatramaphanikumar, K.V. Krishna Kishore
International Journal of Sustainable Agricultural Management and Informatics, 2018
A hybrid framework for detection of diseases in apple and tomato crops with deep feed forward neural network
R. Praneetha, S. Venkatramaphanikumar, K.V. Krishna Kishore
International Journal of Sustainable Agricultural Management and Informatics, 2018
Extended correlated principal component analysis with SVM-PUK in opinion mining
Kollimarla Anusha DEVI, Deepak Chowdary EDARA, Venkatrama Phani Kumar SISTLA, Venkata Krishna Kishore KOLLI
Turkish Journal of Electrical Engineering and Computer Sciences, 2018
Real time streaming data storage and processing using storm and analytics with Hive
D. Surekha, G. Swamy, S Venkatramaphanikumar
Proceedings of 2016 International Conference on Advanced Communication Control and Computing Technologies Icaccct 2016, 2017
M5P model tree in predicting student performance : A case study
S. Chaitanya Kumar, E. Deepak Chowdary, S. Venkatramaphanikumar, K.V. Krishna Kishore
2016 IEEE International Conference on Recent Trends in Electronics Information and Communication Technology Rteict 2016 Proceedings, 2017
Face recognition with modular two dimensional PCA under uncontrolled illumination variations
Venkatramaphanikumar S, K. V. Krishna Kishore
International Journal of Electrical and Computer Engineering, 2016
Ensemble classification technique to detect stress in IT- Professionals
E. Deepak Chowdary, K. Anusha Devi, D. Mounika, S. Venkatramaphanikumar, K.V. Krishna Kishore
Proceedings of the International Conference on Inventive Computation Technologies Icict 2016, 2016
SVM kernel based predictive analytics on faculty performance evaluation
E. Deepak, G. Sai Pooja, R.N.S. Jyothi, S.V. Phani Kumar, K.V. Kishore
Proceedings of the International Conference on Inventive Computation Technologies Icict 2016, 2016
An efficient text dependent speaker recognition using fusion of MFCC and SBC
K V Krishna Kishore, Syed. Sharrefaunnisa, S. Venkatramaphanikumar
2015 1st International Conference on Futuristic Trends in Computational Analysis and Knowledge Management Ablaze 2015, 2015
A novel approach for fingerprint recognition with dynamic time warping
Journal of Theoretical and Applied Information Technology, 2015
Multi feature based fingerprint recognition with score fusion
International Journal of Applied Engineering Research, 2015
A novel prediction model for academic emotional progression of graduates
Arpn Journal of Engineering and Applied Sciences, 2015
Prediction of student academic progression: A case study on Vignan University
K V Krishna Kishore, Venkatramaphanikumar S, Sura Alekhya
2014 International Conference on Computer Communication and Informatics Ushering in Technologies of Tomorrow Today Iccci 2014, 2014
Nonlinear face classification with modified 2DDTW
S. Venkatramaphanikumar, V. Kamakshi Prasad
Proceedings 2014 6th International Conference on Computational Intelligence and Communication Networks Cicn 2014, 2014
Weather based prediction of pests in cotton
K.V. Raghavendra, D. S. Bhupal Naik, S. Venkatramaphanikumar, S. Deva Kumar, S. V. Rama Krishna
Proceedings 2014 6th International Conference on Computational Intelligence and Communication Networks Cicn 2014, 2014
Gabor based face recognition with dynamic time warping
S. Venkatramaphanikumar, V Kamakshi Prasad
2013 6th International Conference on Contemporary Computing Ic3 2013, 2013
An efficient multimodal person authentication system using Gabor and sub band coding
S Venkatramaphanikumar, K V Krishna Kishore
2013 IEEE International Conference on Computational Intelligence and Computing Research IEEE Iccic 2013, 2013
An enhanced face recognition with modular locally discriminating projection
S Venkatrama Phani Kumar, K. V. K Kishore, K. Hemantha Kumar
Icect 2011 2011 3rd International Conference on Electronics Computer Technology, 2011
Hybrid face recognition with locally discriminating projection
Venkatrama Phani Kumar S, K.V.K. Kishore, K. Hemantha Kumar
2010 International Conference on Signal Acquisition and Processing Icsap 2010, 2010

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