Venkata Krishna Kishore Kolli

Scopus Publications

POETIC-NET: an augmented contextual feature representation via graph centrality relation for emotion recognition of English poetry
Praveen Kumar Kazipeta, Venkatrama Phani Kumar Sistla, Venkata Krishna Kishore Kolli
Evolving Systems, 2026
CSBAM-MobileNet: a lightweight attention-enhanced deep learning model for student attentiveness classification
Sajja Radharani, Venkatramaphanikumar Sistla, Venkata Krishna Kishore Kolli
Engineering Science and Technology an International Journal, 2026
Emotions significantly influence behaviour and decision-making, making the assessment of student attentiveness a critical factor in optimizing learning outcomes. This paper introduces a vision-based deep learning framework designed to evaluate student attentiveness through facial expression analysis. Leveraging a Customized lightweight MobileNet architecture enhanced with Channel and Spatial Attention Mechanisms (CSBAM), the model efficiently captures subtle facial cues relevant to attentiveness, such as eye closure, gaze direction, and mouth movements. A custom classroom expression dataset was curated for training, enabling the model to achieve a high accuracy of 91.2%. On more challenging subsets exhibiting varied lighting, occlusion, and head pose variations, the model maintained robust performance with 84.2% accuracy. Evaluation across multiple public benchmark datasets further demonstrated the model’s adaptability, achieving accuracies of 92.5% on DAiSEE, 89.6% on RAF-DB, and 87.4% on FER-2013.This framework offers real-time insights into student engagement, providing a foundation for adaptive and personalized teaching strategies. Future research aims to integrate additional behavioural and physiological cues, such as body posture and voice features, to enable a more comprehensive and multi-modal assessment of student attentiveness. The proposed approach holds promise for enhancing educational interventions and fostering active learning environments.
Enhancing SER Transferability With a Gated Transformer Encoder and Hybrid Loss: A Domain Adaptation Framework
Syed Shareefunnisa, Venkata Krishna Kishore Kolli
IEEE Access, 2026
Most Speech Emotion Recognition (SER) systems do not generalize well across datasets and languages due to domain shift and restricted labeled data. To address these challenges, this work introduces the Domain Adaptation Training framework to enhance the transferability of SER models across heterogeneous speech corpora. The framework leverages XLS-R pre-trained acoustic embeddings and introduces three encoder variants: Gated Transformer Encoder(GTE), Sequential Context Encoder(SCE), and Conformer Adapter Encoder(CAE), to learn domain-invariant and emotion-discriminative features. First, a hybrid multi-objective loss is formulated based on combining Cross-Entropy, Mix-up, Contrastive, Domain-Adversarial, Pseudo-Label, and Entropy losses. This unified loss optimises inter-domain alignment, intra-class compactness, and emotion separability, enabling robust performance even under severe domain mismatches or with limited supervision. Extensive experiments have been carried out on RAVDESS → EMO-DB, IEMOCAP → EMO-DB, EMO-DB → Hindi, and RAVDESS → Hindi under supervised, semi-supervised, and unsupervised settings. In all these cases, the proposed DAT framework has performed better than the baseline models, as measured by accuracy and F1-score. Overall, the results indicate that multi objective domain adaptation significantly enhances both cross-corpus and cross-lingual SER, providing a more robust and transferable solution for real-world applications.
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
TStarGANv2-VC: Non-parallel Multi-domain Transformer Based StarGANv2 Voice Conversion
Anandhakumar Dharmalingam, Venkata Krishna Kishore Kolli
Circuits Systems and Signal Processing, 2025
Message from Convener
Proceedings 2025 2nd International Conference on Networks and Soft Computing Icnsoc 2025, 2025
It is a great honor to welcome you all to the 2nd International Conference on Networks and Soft Computing. I am truly privileged to address this distinguished gathering of scholars, innovators, and thought leaders committed to advancing the frontiers of technology.
Dynamic Aspect Filtering and Relevance Ranking for Enhanced Aspect-Based Sentiment Analysis
V Vamsi Krishna T, K.V. Krishna Kishore
2025 17th IEEE International Conference on Computational Intelligence and Communication Networks Cicn 2025, 2025
Aspect-based sentiment analysis aims to identify the sentiment directed toward a specific aspect within a sentence. Transformer-based approaches provide strong contextual representation but often struggle to eliminate irrelevant tokens, emphasize sentiment-critical aspects, and capture inter-aspect dependencies effectively. To address these limitations, this paper introduces the Aspect Relevance Sentiment Analysis (ARSA) framework, which incorporates five innovative components. These include ACE-RoBERTa for dynamic context encoding based on aspect relevance, Dynamic Aspect Filtering to remove noise and unnecessary words, Aspect Relevance Ranking to highlight key influential aspects, and Multi-Aspect Attention Graphs to capture relationships among aspects. Experiments conducted using the SemEval 2014 benchmark datasets for Laptop and Restaurant domains demonstrate that the proposed model, which focuses on aspect relevance and minimizes noisy information, achieves a substantial improvement in overall precision.
Improved Adaptive Instance Normalization for StarGANv2-VC
Dharmalingam Anandhakumar, K. V. Krishna Kishore
Proceedings of 2025 2nd International Conference on Cognitive Robotics and Intelligent Systems Icc Robins 2025, 2025
Voice Conversion (VC) systems modify vocal style while preserving linguistic information. Their relevance has grown as communication technologies have been more widely used. Deep Learning (DL) models based on Generative Adversarial Networks (GANs) have recently made great strides in VC. Among the numerous GAN models, the StarGANv2-VC includes adversarial source classifier loss and perceptual loss for VC. It uses single set of generators and discriminators to execute VCs across multiple speakers. It incorporates feature statistics with Adaptive Instance Normalization (AdaIN), which effectively modifies the content input's mean and variance to align with the style input. On the other hand, AdaIN applies normalization to the mean and variance of all feature maps separately, which could potentially remove information regarding the relative sizes of features. In view of the above, this work proposes an Improved StarGANv2-VC (IStarGANv2-VC) model, which modifies the AdaIN to perform normalization and modulation to remove artifacts and enhance speech quality. These two operations are only performed on the standard variance alone, whereas the mean is not required. This MAdaIN improves data efficiency in models by normalizing and modifying convolutional weights rather than feature maps, hence reducing information loss. MAdaIN uses speech embedding collected from target speaker data to generate affine variables directly controlling convolutional weights. Thus, this technique helps maintain essential elements while improving VC performance, specifically converted speech quality and similarity. Finally, test results using various benchmark corpora prove that the IStarGANv2-VC achieves higher efficiency and quality of converted speech, especially based on Root Mean Square Error (RMSE), Mel Cepstral Distortion (MCD), Mean Opinion Score (MOS), Word Error Rate (WER), Character Error Rate (CER), and voice similarity score.
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.
Biogas Production Using Flexible Biodigester to Foster Sustainable Livelihood Improvement in Rural Households †
Charles David, Venkata Krishna Kishore Kolli, Karpagaraj Anbalagan
Engineering Proceedings, 2025
With the global emphasis on sustainable growth and development, the depletion of natural energy reserves due to reliance on fossil fuels and non-renewable sources remains a critical concern. Despite strides in transitioning to electrical mobility, rural and agricultural communities depend heavily on liquefied petroleum gas and firewood for cooking, lacking viable, sustainable alternatives. This study focuses on community-led efforts to advance biogas adoption, providing an eco-friendly and reliable energy alternative for rural and farming households. By designing and developing balloon-type anaerobic biodigesters, this initiative provides a robust, cost-effective, and scalable method to convert farm waste into biogas for household cooking. This approach reduces reliance on traditional fuels, mitigating deforestation and improving air quality, and generates organic biofertilizer as a byproduct, enhancing agricultural productivity through organic farming. The study focuses on optimizing critical parameters, including the input feed rate, gas production patterns, holding time, biodigester health, gas quality, and liquid manure yield. Statistical tools, such as descriptive analysis, regression analysis, and ANOVA, were employed to validate and predict biogas output data based on experimental and industrial-scale data. Artificial neural networks (ANNs) were also utilized to model and predict outputs, inspired by the information processing mechanisms of biological neural systems. A comprehensive database was developed from experimental and literary data to enhance model accuracy. The results demonstrate significant improvements in cooking practices, health outcomes, economic stability, and solid waste management among beneficiaries. The integration of statistical analysis and ANN modeling validated the biodigester system’s effectiveness and scalability. This research highlights the potential to harness renewable energy to address socio-economic challenges in rural areas, paving the way for a sustainable, equitable future by fostering environmentally conscious practices, clean energy access, and enhanced agricultural productivity.
Evaluation of Customized Pre-trained Models to Predict Percentage of Being COVID-19 Cases on Chest X-Ray and COVID Radiography Images
Sujatha Kamepalli, Kolli Venkata Krishna Kishore, Bandaru Srinivasa Rao
Lecture Notes in Networks and Systems, 2025
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
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
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
Context-Aware Automated Essay Scoring with MLM-Pretrained T5 Transformer
Chavva Ravi Kishore Reddy, Venkata Krishna Kishore K, Arjun Kireeti Tulasi, Manideep Maturi, Abhiram Nagam
Proceedings of the 6th International Conference on Inventive Research in Computing Applications Icirca 2025, 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
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
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
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
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
A Multi-Algorithm Stacking Approach to Lung Cancer Detection with SVM, GBM, Naive Bayes, Decision Tree, and Random Forest Models
Deepthi Alla, Sruthi Bajjuri, Vijaya Lakshmi, Bala Chandu Dasari, S Deva Kumar, Venkata Krishna Kishore Kolli
IEEE International Conference on Computational Communication and Information Technology Icccit 2025, 2025
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
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
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
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
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
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
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 hierarchical framework for plant leaf disease detection using residual vision transformer
Sasikala Vallabhajosyula, Venkatramaphanikumar Sistla, Venkata Krishna Kishore Kolli
Heliyon, 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
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
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
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
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
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
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 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
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
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
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 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
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
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
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
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
Performance evaluation of DNN with other machine learning techniques in a cluster using Apache Spark and MLlib
A.N.M. JayaLakshmi, K.V. Krishna Kishore
Journal of King Saud University Computer and Information Sciences, 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
Multi-Class Classification and Prediction of Heart Sounds Using Stacked LSTM to Detect Heart Sound Abnormalities
Sujatha Kamepalli, Bandaru Srinivasa Rao, Kolli Venkata Krishna Kishore
2022 3rd International Conference for Emerging Technology Incet 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
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
Sentiment Classification of English and Hindi Music Lyrics Using Supervised Machine Learning Algorithms
Sumith N., Shruti Wagle, Priyanka Ghosh, Karishma Kishore
2022 2nd Asian Conference on Innovation in Technology Asiancon 2022, 2022
Virtual facial expression recognition using deep CNN with ensemble learning
Venkata Rami Reddy Chirra, Srinivasulu Reddy Uyyala, Venkata Krishna Kishore Kolli
Journal of Ambient Intelligence and Humanized Computing, 2021
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
Animal Breed Classification and Prediction Using Convolutional Neural Network Primates as a Case Study
Sujatha Kamepalli, Venkata Krishna Kishore Kolli, Srinivasa Rao Bandaru
2021 4th International Conference on Electrical Computer and Communication Technologies Icecct 2021, 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
Performance of Machine Learning Algorithms in Red Wine Quality Classification based on Chemical Compositions
Indian Journal of Ecology, 2020
Predictive model for network intrusion detection system using deep learning
Venkatramaphanikumar Sstla, Venkata Kolli, Lakshmi Voggu
Revue D Intelligence Artificielle, 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
Analyzing factors influencing the research progression of faculty in STEM based universities
International Journal of Advanced Science and Technology, 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
Facial emotion recognition using NLPCA and SVM
Chirra Venkata Rami Reddy, Uyyala Srinivasulu Reddy, Kolli Venkata Krishna Kishore
Traitement Du Signal, 2019
Scale invariant face recognition with gabor wavelets and SVM
International Journal of Recent Technology and Engineering, 2019
Deep CNN: A machine learning approach for driver drowsiness detection based on eye state
Venkata Chirra, Srinivasulu ReddyUyyala, Venkata KishoreKolli
Revue D Intelligence Artificielle, 2019
Prognosis of Diseases Using Machine Learning Algorithms: A Survey
N. Marline Joys Kumari, Kishore K.V. Krishna
Proceedings of the 2018 International Conference on Current Trends Towards Converging Technologies Icctct 2018, 2018
Prediction of Cervical Cancer using Voting and DNN Classifiers
Komala Rayavarapu, Kishore K.V. Krishna
Proceedings of the 2018 International Conference on Current Trends Towards Converging Technologies Icctct 2018, 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
Privacy preservation of sink node location in wireless sensor network using RFSN-RSA
Kolli Kishore, Pondugala Kumar, Dondeti Venketasulu
Advances in Modelling and Analysis B, 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
Person identification system using feature level fusion of multi-biometrics
Ch. Venkata Rami Reddy, K.V. Krishna Kishore, U. Srinivasulu Reddy, M. Suneetha
2016 IEEE International Conference on Computational Intelligence and Computing Research Iccic 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
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
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
Medical image denoising using KPCA with local pixel grouping
T. Gopi Rambabu, K. V. Krishna Kishore
2015 International Conference on Computer Communication and Informatics Iccci 2015, 2015
A novel E-learning framework to learn IT skills for visual impaired
K. V. Krishna Kishore, A. Raghunath
2015 1st International Conference on Futuristic Trends in Computational Analysis and Knowledge Management Ablaze 2015, 2015
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 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
Hybrid framework for face recognition with expression & illumination variations
K.V. Krishna Kishore, G P S Varma
Proceeding of the IEEE International Conference on Green Computing Communication and Electrical Engineering Icgccee 2014, 2014
General chair message
1st International Conference on Networks and Soft Computing Icnsc 2014 Proceedings, 2014
A novel image denoising using kernel principle component analysis with dual phase BMLPG
International Journal of Applied Engineering Research, 2014
Continues blood pressure measurement and data logging device with SMS alert
M. K. Chaithanya, K. V. K. Kishore, Avireni Srinivasulu
International Journal of Multimedia and Ubiquitous Engineering, 2014
Blood pressure measurement and data storing using volume compensation method
M. K. Chaithanya, Avireni Srinivasulu, K. V. K. Kishore
2014 International Conference on Devices Circuits and Communications Icdccom 2014 Proceedings, 2014
Performance estimation of DSR, DSDV and AODV in TCP, UDP and SCTP
B. Suvarna, K V Krishna Kishore, G. Parimala, R. Prathap Kumar
Icroit 2014 Proceedings of the 2014 International Conference on Reliability Optimization and Information Technology, 2014
Multi-Feature fusion based facial expression classification using DLBP and DCT
International Journal of Software Engineering and Its Applications, 2014
Emotion recognition in speech using MFCC and wavelet features
K. V. Krishna Kishore, P. Krishna Satish
Proceedings of the 2013 3rd IEEE International Advance Computing Conference Iacc 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
Facial expression classification using Kernel based PCA with fused DCT and GWT features
Ch. V. Ramireddy, 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
An intelligent authentication system using wavelet fusion of K-PCA, R-LDA
Jyostna Devi Bodapati, K.V.K. Kishore, N. Veeranjaneyulu
2010 IEEE International Conference on Communication Control and Computing Technologies Icccct 2010, 2010
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
A novel face recognition system based on combining eigenfaces with fisher faces using wavelets
B. Jyostna Devi, N. Veeranjaneyulu, K.V.K. Kishore
Procedia Computer Science, 2010

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