Journals & Conferences (CSE)
Mr Ch. Krishna Rao A Web-Based Automation Framework for Instagram Profile Forensics.DOI- 10.54380/IJRDET1225_148
Mrs.K Prajwala An Electricity Generation Forecasting : A Hybrid Deep Learning Approach Using Rnn-Bilstm Fusion
Dr.Aparna Rajesh Atmakuri A Hybrid Framework for Predicting Stock Market Volatility Using Financial Models and Machine Learning - DOI- 10.1109/ISCON65210.2025.11340805
Dr.Aparna Rajesh Atmakuri Automated Preprocessing and Deep Learning for Non-Invasive Anemia Estimation Using Conjunctival Images - DOI- 10.1109/GIEST66547.2025.11387069
Dr.P.Balakrishna A Machine-Learning (ML)-Based Conventional Charge Management System Guiding Electric Cars (Evs) To Charging Stations - DOI- 10.1109/ICCAMS65118.2025.11233910
Mrs Swapna Vanguru A Novel Image Spam Detection Framework using Hybrid Deep Learning Model
Mrs.B. Mamatha HomeScape: An Integrated Mobile Application for Interior Design and Service Management.
Mrs.D.Mounika Enhancing Digital Content Accessibility Through Intelligent Video Summarization Systems
Mrs.D.Mounika A comparative study of hyper parameter tuning methods for diabetes prediction using extra tree classifier
Mrs.P.Jeevan Jyothi Spatio-Temporal Graph Neural Networks (ST-GNN) for dynamic relationships and analysis of stream data sets
Dr.Aparna Rajesh Atmakuri Deep learning unlocked: hands on guide from fundamentals to real world
Dr.P.Balakrishna Digital System Design and Applications
Mrs.P.Jeevan Jyothi Deep Learning and Generative AI for Data-Driven Computing
Dr.Aparna Rajesh Atmakuri Hybrid Deep Learning System and method for Enhanced Breast cancer Diagnosis from Medical Imaging data.
Mrs Swapna Vanguru A Beginner's Journey With Regression Analysis Using The Boston Housing Dataset For Predicting Home Prices
Mrs.K Prajwala Railway Track Defect Detection Using ans Unsupervised Learning Model.
Mrs.N Malathi Deep Learning model for Image based Multiclass weather condition identification.
Dr Pulluri Srinivas Rao Transfer Reinforcement Learning-Based Cross-Domain Recommendation of Charging Stations in the Internet of Vehicles - DOI- https://doi.org/10.25103/jestr.193.09.
Dr Pulluri Srinivas Rao Advanced Error Correction Techniques in Digital Communication Using Viterbi Decoder - DOI- https://doi.org/10.25103/jestr.193.22
Dr.Aparna Rajesh Atmakuri RLeHLDD: A reinforcement learning enabled Human-in-the-Loop framework for multi-domain Deepfake Detection approach - DOI-10.1016/j.aei.2026.104861
Dr.Ch.Rathan Kumar Automated Raga Identification System with Web Scraping Framework - DOI- https://doi.org/10.18280/isi.310421
Mrs Swapna Vanguru NeuroScan AI: Deep Learning for Brain Tumor Analysis - DOI- doi.org/20.14148/kkj/v26.5216.
Dr.Aparna Rajesh Atmakuri Enhancing Digital Forensic Chain-of-Custody Integrity through Blockchain: Hash-based Attribution and Workflow Anomaly Analysis - DOI-10.1109/ICICT68280.2026.11511004.
Dr.Aparna Rajesh Atmakuri Two-Layer Input Filtering Framework for Large Language Model Security - DOI-10.1109/I3CTCON68242.2026.11507202.
Dr.Aparna Rajesh Atmakuri Deepsea: A Deep Learning-Based Secure and Energy-Aware Adaptive Protocol for Underwater Sensor Networks - DOI-10.1109/ICSADL67539.2026.11451779.
Dr.P.Balakrishna The impact of representation choice in clinical text classification tasks - DOI-10.1109/GSEACT68539.2026.11620514.
Mrs Swapna Vanguru An Intelligent Decentralized Secure File Sharing System with Privacy-Preserving Access Control - DOI- PGGiTech - ICSSEECC 2026.
Mrs Swapna Vanguru Smart Farming Through Deep Learning: Automated Plant Disease Detection Using CNN Models.
Mrs.K Prajwala Enhancing Time Series Stock Predictions Using GANs with Technical Indicators and Twitter Sentiment Analysis - DOI- https://doi.org/10.1063/5.0341963.
Dr.Aparna Rajesh Atmakuri Cyber Defense in the Quantam AGE.
Mrs.D.Mounika Optimized Deep Learning Approach for Alzheimer’s Prediction Using CNN on MRI Images - DOI- DOI: 10.1002/9781394315727.ch12.
Dr. Dhananjay Adaptive Federated Machine Learning Framework for Early Multi-Disease Prediction Using Wearable Sensor Networks.
Dr.Aparna Rajesh Atmakuri Machine Learning model for cybersecurity intelligent data autmotation.
Breaking the Glass Ceiling S. Siddamsetti and K. Deepthi, “Breaking the Glass Ceiling: Empowering Women from Margins to Mainstreams,” The Review of Contemporary Scientific and Academic Studies, vol. 3, no. 6, Jun. 2023, DOI: 10.55454/rcsas.3.06.2023.004.
A Comprehensive Review on Deep Learning Techniques V. Madhavi and P. Lalitha Surya Kumari, “A Comprehensive Review on Deep Learning Techniques Used in Diagnosing Retinal Diseases on Fundus Images,” in Futuristic Trends in Artificial Intelligence, vol. 3, May 2023, pp. 121–136, DOI: 10.58532/V3BKAI3P3CH1.
Dynamic RL-ACO S. Nimmala, M. Ramchander, M. Mahendar, P. Manasa, D. D. Bhavani, and K. Raghavendar, “Dynamic RL-ACO: Reinforcement Learning-based Ant Colony Optimization for Load Balancing in Cloud Networks,” in 2024 5th International Conference on Smart Electronics and Communication (ICOSEC), Trichy, India, 2024, pp. 475–480, DOI: 10.1109/ICOSEC61587.2024.10722410.
Fuzzy-Enhanced XGBoost Model S. Nimmala et al., “Fuzzy-Enhanced XGBoost Model for Classifying Kidney Disease Severity,” in 2024 4th International Conference on Soft Computing for Security Applications (ICSCSA), 2024, DOI: 10.1109/ICSCSA64454.2024.00011.
Emotion Estimation Model M. Mahendar, A. Malik, and I. Batra, “Emotion estimation model for cognitive state analysis of learners in online education using deep learning,” Expert Systems, vol. 42, no. 1, e13289, 2025, DOI: 10.1111/exsy.13289.
Tweet Spam Detection P. Manasa, A. Malik, K. N. Alqahtani, M. A. Alomar, M. S. Basingab, M. Soni, A. Rizwan, and I. Batra, “Tweet Spam Detection Using Machine Learning and Swarm Optimization Techniques,” IEEE Transactions on Computational Social Systems, vol. 11, no. 4, pp. 4870–4877, Aug. 2024, DOI: 10.1109/TCSS.2022.3230823.
Depression Symptom Identification P. V. Narayanrao, K. Kohirker, T. S. Preeth, and P. L. S. Kumari, “Depression Symptom Identification Through Acoustic Speech Analysis: A Transfer Learning Approach,” Traitement du Signal, vol. 41, no. 1, pp. 165–177, Feb. 2024, DOI: 10.18280/ts.410113.
Facial Micro-expression Modelling M. Mahendar, A. Malik, and I. Batra, “Facial Micro-expression Modelling-Based Student Learning Rate Evaluation Using VGG–CNN Transfer Learning Model,” SN Computer Science, vol. 5, no. 2, Art. no. 204, 2024, DOI: 10.1007/s42979-023-02519-0.
A Recent Survey on AI Enabled Practices for Smart Agriculture S. Nimmala, M. Ramchander, M. Mahendar, P. Manasa, M. A. Kiran, and B. Rambabu, “A Recent Survey on AI Enabled Practices for Smart Agriculture,” in 2024 International Conference on Intelligent Systems for Cybersecurity (ISCS), 2024, DOI: 10.1109/ISCS61804.2024.10581009.
A Comparative Analysis for Deep-Learning-Based Approaches R. Ch, M. Radha, M. Mahendar, and P. Manasa, “A comparative analysis for deep-learning-based approaches for image forgery detection,” International Journal of Systematic Innovation, vol. 8, no. 1, pp. 1–10, 2024, DOI: 10.6977/IJoSI.202403_8(1).0001.
Detection of Twitter Spam Using GLoVe P. Manasa, A. Malik, and I. Batra, “Detection of Twitter Spam Using GLoVe Vocabulary Features, Bidirectional LSTM and Convolution Neural Network,” SN Computer Science, vol. 5, no. 2, Art. no. 206, 2024, DOI: 10.1007/s42979-023-02518-1.
Systems-wide View of Host-Pathogen Interactions M. S. Singh, A. Pyati, R. D. Rubi, R. Subramanian, V. Y. Muley, M. A. Ansari, and S. Yellaboina, “Systems-wide view of host-pathogen interactions across COVID-19 severities using integrated omics analysis,” iScience, vol. 27, no. 3, Art. no. 109087, Mar. 2024, DOI: 10.1016/j.isci.2024.109087.
Building an Emotion Detection System R. Srilakshmi et al., “Building an Emotion Detection System in Python Using Multi-Layer Perceptrons for Speech Analysis,” in 2023 3rd International Conference on Technological Advancements in Computational Sciences (ICTACS), 2023, DOI: 10.1109/ICTACS59847.2023.10390529.
Detection of Anomalies in Blockchain S. Siddamsetti and M. Srivenkatesh, “Detection of Anomalies in Blockchain Using Federated Learning in IoT Devices,” Journal of Theoretical and Applied Information Technology, vol. 101, no. 18, Sep. 2023.
Comparative Study of Cyber Security Risk Assessment Frameworks S. Siddamsetti and R. Subramanian, “Comparative Study of Cyber Security Risk Assessment Frameworks,” NeuroQuantology, vol. 21, no. 6, pp. 2015–2024, Jun. 2023, DOI: 10.48047/nq.2023.21.6.nq23199.
Efficient Fraud Detection in Ethereum Blockchain S. Siddamsetti and M. Srivenkatesh, “Efficient Fraud Detection in Ethereum Blockchain through Machine Learning and Deep Learning Approaches,” International Journal on Recent and Innovation Trends in Computing and Communication, vol. 11, no. 11s, pp. 71–82, Oct. 2023, DOI: 10.17762/ijritcc.v11i11s.8072.
A System for Analysing Call Drop Dynamics C. Balakrishna, C. Ramesh, S. Meghana, and C. Dastagiraiah, “A System for Analysing Call Drop Dynamics in the Telecom Industry Using Machine Learning and Feature Selection,” Journal of Theoretical and Applied Information Technology, vol. 102, no. 22, pp. 8034–8049, Nov. 2024.
Enrichment of Retinal Fundus Images V. M. V. P. Madhavi and P. Lalitha Surya Kumari, “Enrichment of Retinal Fundus Images using EN-CLAHE and Auto-CLAHE Methods,” International Journal of Intelligent Systems and Applications in Engineering, vol. 12, no. 3, pp. 1213–1221, Mar. 2024.
Predicting Depression Risk from Facial Video-Derived Heart Rate Estimates P. V. Narayanrao and P. L. S. Kumari, “Predicting Depression Risk from Facial Video-Derived Heart Rate Estimates,” Ingénierie des Systèmes d'Information, vol. 37, no. 4, pp. 997–1004, Aug. 2023, DOI: 10.18280/ria.370421.
Improving Accuracy in Predicting Stress Levels P. V. Narayanrao, R. Srilakshmi, M. Deepika, and P. Lalitha Surya Kumari, “Improving Accuracy in Predicting Stress Levels of Working Women Using Convolutional Neural Networks,” in Optimized Predictive Models in Healthcare Using Machine Learning, Wiley, pp. 39–56, 2024, DOI: 10.1002/9781394175376.ch3.
Early Glaucoma Detection V. M. Vuppu and P. Lalitha Surya Kumari, “Early Glaucoma Detection using LSTM-CNN integrated with Multi Class SVM,” Engineering, Technology & Applied Science Research, vol. 14, no. 4, pp. 15645–15650, Aug. 2024, DOI: 10.48084/etasr.7798.
Heart Disease Prediction System V. Krishnaiah, “Heart Disease Prediction System Using Convolutional Neural Networks,” International Journal of Computer Sciences and Engineering, vol. 12, no. 1, pp. 8–15, Jan. 2024, DOI: 10.26438/ijcse/v12i1.815.
Personalized Ontology and Deep Training Tree-Based GRU-RNN F. M. H. Fernandez, T. Venkata Ramana, M. Shabana, V. Kannagi, and M. Nalini, “Personalized ontology and deep training tree-based optimal gated recurrent unit-recurrent neural network for prediction of students' behavior,” Concurrency and Computation: Practice and Experience, vol. 35, no. 1, Art. no. e7420, 2023, DOI: 10.1002/cpe.7420.
Sangraha360: An Unknown Malware Detection Framework with Federated Learning and Drift Detection S. Mallam, G. R. Balaji, G. V. Reddy, K. Tejaswi, V. Deshmukh, K. Bajrang, and R. Subramanian, “Sangraha360: An Unknown Malware Detection Framework with Federated Learning and Drift Detection,” International Journal of Intelligent Systems and Applications in Engineering, vol. 12, no. 18s, pp. 340–347, Mar. 2024.
Mobile Based Ayurvedic Leaf Detection R. Subramanian, D. R. Rajasekaran, S. Seethamraju, G. Sai Charan, and B. O. Surampalli, “Mobile Based Ayurvedic Leaf Detection and Retrieving Its Medicinal Properties Using Deep Learning and NLP,” International Journal of Intelligent Systems and Applications in Engineering, vol. 12, no. 3, pp. 3561–3565, Mar. 2024.
YOLOv5 AI Deep Learning Model R. Subramanian, R. D. Rubi, R. Tapadia, A. Somani, V. V. R. K. R. Ponugoti, and L. K. R. Kondam, “Yolov5 AI Deep Learning model driven Nuclear Pleomorphism Grading on Breast Cancer Pathology WSI for Nottingham Cancer Grading,” International Journal on Recent and Innovation Trends in Computing and Communication, vol. 11, no. 10, pp. 61–65, Nov. 2023, DOI: 10.17762/ijritcc.v11i10.8465.
Dynamic tumor analysis using deep embedded clustering (DEC) for personalized oncology treatment M. Mahendar, P. Manasa, S. Nimmala, K. Raghavendar, A. N. Sheikh, and M. A. Kiran, “Dynamic tumor analysis using deep embedded clustering (DEC) for personalized oncology treatment,” in 2025 2nd International Conference on Machine Learning and Autonomous Systems (ICMLAS), 2025, pp. 699–703, DOI: 10.1109/ICMLAS64557.2025.10968766.
Early detection and prevention of hypoglycemic episodes in diabetics using random forest algorithm P. Manasa, M. Mahendar, K. Raghavendar, S. Nimmala, R. Ch, and D. D. Bhavani, “Early detection and prevention of hypoglycemic episodes in diabetics using random forest algorithm,” in 2025 4th International Conference on Sentiment Analysis and Deep Learning (ICSADL), 2025, pp. 805–809, DOI: 10.1109/ICSADL65848.2025.10933483.
Honey bee-inspired energy-efficient cluster head optimization for large-scale cloud-based IoT applications S. Nimmala, K. Narasimhulu, M. A. Pasha, R. Ravinder Reddy, M. Mahendar, and P. Manasa, “Honey bee-inspired energy-efficient cluster head optimization for large-scale cloud-based IoT applications,” in 2025 5th International Conference on Expert Clouds and Applications (ICOECA), 2025, pp. 326–331, DOI: 10.1109/ICOECA66273.2025.00063.
An Intelli BEF: An intelligent bio-inspired energy-efficient and fault-tolerant routing for IoT-enabled WSNs S. Nimmala, M. Mahendar, P. Manasa, K. Raghavendar, B. Rambabu, and D. D. Bhavani, “An Intelli BEF: An intelligent bio-inspired energy-efficient and fault-tolerant routing for IoT-enabled WSNs,” in 2025 6th International Conference on Mobile Computing and Sustainable Informatics (ICMCSI), 2025, pp. 942–947, DOI: 10.1109/ICMCSI64620.2025.10883389.
Sentiment analysis in social media: A machine learning perspective T. Hymavathi, R. Srilakshmi, K. B. Rekha, P. A. A. Saleem, and G. Prathyusha, “Sentiment analysis in social media: A machine learning perspective,” in 2025 International Conference on Sustainability, Innovation and Technology (ICSIT), 2025, DOI: 10.1109/ICSIT65336.2025.11295028.
Adaptive Federated Learning for Pneumonia Classification using Chest X-Ray Images
Automated Classification of Atrial and Ventricular Fibrillation Using Integration of Circulant Singular Spectrum Analysis with Four-Stage Savitzky-Golay and ConvMixer.
R. Deepthi, S. Siddamsetti, D. Mallampati, P. Niharika, and T. R. Reddy, “Automatic facial expression recognition based on improved grey wolf optimization algorithm with AEISOM classifier,” Signal, Image and Video Processing, vol. 19, no. 13, p. 1086, 2025, DOI: 10.1007/s11760-025-04605-7.
S. Meghana, D. L. Padmaja, K. S. Gundu, R. Kudari, J. Somasekar, and L. Chandran, “LiDAR image-based Earth carbon emission analysis and its impact on public health: A machine learning model,” Remote Sensing in Earth Systems Sciences, vol. 8, no. 2, pp. 455–464, 2025, DOI: 10.1007/s41976-025-00196-6.
C. Balakrishna, C. Ramesh, S. Meghana, and C. Dastagiraiah, “A system for analysing call drop dynamics in the telecom industry using machine learning and feature selection,” Journal of Theoretical and Applied Information Technology, vol. 102, no. 22, pp. 8034–8049, 2024.
A. Brahmareddy, S. Meghana, S. V. Kiran, K. S. Bharathi, and B. V. Kumar, “Hybrid ensemble deep neural network for intrusion detection (HEDNN-ID),” International Journal of Electronics and Communication Engineering, vol. 12, no. 7, pp. 184–200, 2025, DOI: 10.14445/23488549/IJECE-V12I7P114.
A. Sham, N. B. Nethravathi, S. Sandhyarani, S. Girija Rani, A. S., and A. D. K. Amog Patel, “Advancements in blockchain technology for real estate transactions: A comprehensive overview.”
Identifying Vulnerable Nodes in Networked Infrastructures: A Methodological Approach to Optimal Cost Attacks
S. B. Kolluru, R. Srilakshmi, S. Davuluri, V. Boppana, S. G. Gundabatini, and S. H. Nallamala, “Optimizing security and performance in NOMA networks using machine learning,” Cluster Computing, vol. 28, no. 13, Art. no. 834, 2025, DOI: 10.1007/s10586-025-05524-5.
S. Kousalya, N. Mala, K. J. Eldho, S. Swapna, M. Thamizhsudar, E. Kungumaraj, and G. Jenitha, “Applications and future directions of fuzzy BRK topological groups in mathematics and AI,” Communications on Applied Nonlinear Analysis, vol. 32, no. 3s, pp. 711–716, 2025, DOI: 10.52783/cana.v32.2729.
K. Rajesh, V. Krishnamoorthy, S. Swapna, R. Venugopal, V. Madhuri, R. Uma, and R. Sudha, “Applications of disease identification in the advancement of intuitionistic fuzzy sets with interval,” Communications on Applied Nonlinear Analysis, vol. 32, no. 6s, pp. 26–38, 2025, DOI: 10.52783/cana.v32.3239.
N. Balakumar, L. Ramesh, S. Swapna, V. Appalakonda, K. Jose Reena, P. Tamilselvi, and M. Jenifer, “IoTGuard: A lightweight and energy-efficient encryption algorithm for secure data transmission in IoT networks,” South Eastern European Journal of Public Health, vol. XXVI, S1, pp. 2580–2587, 2025, DOI: 10.70135/seejph.vi.4209.
N. J. B. Noorbhasha, V. Kamma, R. Logesh Babu, J. William Andrews, T. V. Rajani Kanth, and J. R. Vasanthi, “Innovative computational intelligence frameworks for complex problem solving and optimization,” International Journal of Computational and Experimental Science and Engineering, vol. 11, no. 1, pp. 352–363, 2025, DOI: 10.22399/ijcesen.834.
V. Kamma, S. Wbaid, K. Sudhakar, A. Nisha, and T. Saravanan, “Toxic comment detection based on improved bidirectional long short-term memory,” in 2025 International Conference on Intelligent Systems and Computational Networks (ICISCN), 2025.
P. Vaishali and M. S. Sure Mamatha, “Water quality analysis and prediction using random forest and naive Bayes,” in 2025 16th International Conference on Advances in Computing, Control, and Telecommunication Technologies (ACT), 2025, pp. 7999–8005.
V. Kamma, R. Riad Hwsein, M. Lakshmanan, T. Shanthi, and another coauthor, “Face recognition and emotion detection in video streams using Swin Transformer and EfficientNetV2,” in 2025 4th International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE), 2025, DOI: 10.1109/ICDCECE65353.2025.11034959.
V. Hari Prasad, M. Rafi, M. K. Ahmed, A. Ben Miled, and M. Shabana, “Random self-generative Schnorr certificate less signcryption for secure data sharing in mobile cloud environments,” WSEAS Transactions on Computer Research, vol. 13, pp. 270–280, 2025, DOI: 10.37394/232018.2025.13.25.
S. Siddamsetti, “Email spam filtering model with the machine learning models,” Communications on Applied Nonlinear Analysis, vol. 32, no. 9s, pp. 2610–2614, 2025, DOI: 10.52783/cana.v32.4564.
V. Vishwarupe, A. Hankey, S. Pangaonkar, S. Shekhar, et al., “Predicting mental health ailments using social media activities and keystroke dynamics with machine learning,” in Big Data in Finance: Transforming the Financial Landscape, 2025, pp. 33–44, DOI: 10.1007/978-3-031-80656-8_4.
V. Kamma, Y. H. Bhosale, M. Kumar, M. M. M. Kumar, M. Arif, and N. Anitha, “Integrating IoT and cloud analytics for real-time monitoring of post-surgical recovery in maxillofacial patients,” Vascular and Endovascular Review, vol. 8, no. 1s, pp. 304–311, 2025.
S. Swapna, D. Gulyamova, B. S. Khalaf, S. T. Gopukumar, H. Kadhim, and A. Balakumar, “Deep reinforcement learning and capsule networks for advanced bone cancer detection,” in 2025 IEEE 2nd International Conference on Advances in Modern Age Technologies for Health and Engineering Science (AMATHE), 2025, DOI: 10.1109/AMATHE65477.2025.11080806.
S. Swapna, N. Roopalatha, L. Chenniappan, D. Nimma, et al., “Enhancing HR onboarding efficiency and predicting employee success with graph neural network,” in 2024 4th International Conference on Innovative Sustainable Computational Technologies (CISCT), 2024, DOI: 10.1109/CISCT62494.2024.11134182.
B. Nethravathi, G. R. Suthoju, B. C. Kavitha, M. K. Bindiya, B. Madhu, B. R. Harsha, D. S. Deshpande, B. Y. Rakshitha, and S. Gokul, “An AI-augmented kernel for dynamic resource utilization in virtualized environments,” Engineering, Technology & Applied Science Research, vol. 15, no. 5, pp. 26959–26964, Oct. 2025, DOI: 10.48084/etasr.12536.
On the sample complexity of actor-critic method for reinforcement learning with function approximation” by H. Kumar, A. Koppel, and A. Ribeiro, Machine Learning, vol. 112, pp. 2433–2467, 2023, DOI: 10.1007/s10994-023-06303-2.
Biological Hydrogen Production and Advanced Bioreactor Integration for Eco-Friendly Fuel Cell Applications in Sustainable Energy Solution
U. L. Soundarya, G. K. Chaitanya, R. Srilakshmi, K. S. Faizz Ahmad, et al., “GAIT based human identification using modified parallel convolutional neural network with BIRCH segmentation,” 2025. DOI: 10.2139/ssrn.5434604.
Enhanced Human Identification System Using Optimized Ensemble Classifiers and Gait Pattern
Score Level Fused Hybrid Architecture for GAIT Based Human Identification with Modified U-NET Segmentation Model
V. Kamma, S. Choudhary, S. Kumar, S. Gowroju, S. S. Badhiye, and K. B. Prakash, “Smart inquiry management: Leveraging machine learning and NLP for Industry 5.0 advancements,” in 2025 4th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0, 2025, pp. 1–6, DOI: 10.1109/OTCON65728.2025.11070686.
V. Kamma, S. Choudhary, S. Kumar, S. Gowroju, et al., “Extracting structured medical insights from clinical texts using NLP,” in IV International Conference ‘Sustainable Development: Engineering in Agriculture, Energy and Materials Science’ (VMAEE-IV-2025), 2026, DOI: 10.1063/5.0326220.
H. M. Manoj, D. L. Shanthi, B. N. Lakshmi, K. J. Archana, E. V. N. Jyothi, and K. Archana, “AI-driven drone technology and computer vision for early detection of crop disease in large agricultural areas,” Scientific Reports, vol. 16, Art. no. 2479, 2026, DOI: 10.1038/s41598-025-32384-1.
S. Mallam and K. V. Ranga Rao, “Human-AI voice interaction in clinical contexts: Designing emotionally responsive systems for mental health applications,” Journal of Zunith University, vol. 26, no. 5, pp. 445–454, 2026, DOI: 10.5281/20393809.
R. Ali, M. Shabana, M. Mohammed, K. Masthan, et al., “Continuous learning enabled dual attention-based fairness-aware explainable convolutional network for bias mitigation in a critical decision system for human action recognition,” Engineering Applications of Artificial Intelligence, vol. 172, Art. no. 114251, 2026, DOI: 10.1016/j.engappai.2026.114251.
P. Manasa, M. Mahendar, P. V. Narayanrao, S. Komuravelli, S. Lakhani, S. Basheer, and M. T. Quasim, “Big data-driven video anomaly detection using VideoMAE for visual analytics in CCTV surveillance,” Big Data, vol. 14, no. 4, pp. 291–303, 2026, DOI: 10.1177/2167647X261463938.
S. Bethu, S. Lakumarapu, and S. Mahammad, “Deep learning based human identification using IoT surveillance network systems,” SN Computer Science, vol. 7, no. 4, Art. no. 341, 2026, DOI: 10.1007/s42979-026-04935-4.
“EnviGeoOpt-ML: A mechanism-grounded spatio-temporal machine learning framework for site-specific earthquake ground-motion forecasting,” 2026. [Complete author, venue, volume, pages, and DOI could not be reliably verified.]
E. Mahender, R. S. Kumar, S. D. L. Shanthi, G. Bhavya, K. Manasa, et al., “Yield2ActionAI for calibrated multimodal crop yield forecasting and uncertainty aware resource optimization in precision farming,” Discover Computing, vol. 29, Art. no. 535, 2026, DOI: 10.1007/s10791-026-10465-7.
“AI-empowered task offloading for delay-sensitive IoT in edge computing networks: A survey,” 2026. [Exact paper could not be reliably matched to a publication record.]
Virus threat on heart,” [Insufficient bibliographic information to prepare a reliable IEEE citation.]
C. V. K. Reddy, V. K. Chandrasekaran, G. Koduru, L. Guganathan, et al., “Adaptive energy optimization for PV-integrated EV parking lots in a reconfigurable microgrid based on the Serval optimization and multimodal adaptive spatio-temporal graph neural network,” Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 2026, DOI: 10.1007/s40998-026-01020-7.
S. Pathan, D. Kandagatla, T. Malathi, S. I. Fatima, V. K. Gugulothu, and P. V. Narayanrao, “An interpretable multi-modal ensemble framework for breast cancer analysis using imaging, omics and biomedical literature,” International Journal of Online and Biomedical Engineering, vol. 22, no. 5, pp. 124–138, 2026, DOI: 10.3991/ijoe.v22i05.60535.
C. Kate, T. A. S. Srinivas, M. Asim, M. S. Kumar, P. V. Narayanrao, and S. Sasi, “EduBoost: A high-accuracy XGBoost framework for student academic performance prediction,” Journal of Engineering and Technology for Industrial Applications, 2026, DOI: 10.5935/jetia.v12i59.3734.
S. Khullar, P. V. Narayanrao, P. S. Poojitha, S. Reddy Kollem, P. S. Shankar, and S. Kaliappan, “Real-time histopathological cancer diagnosis using ResNet50: Transfer learning for automated tumor detection and classification,” in 2026 7th International Conference on Mobile Computing and Sustainable Informatics (ICMCSI), 2026, pp. 658–665, DOI: 10.1109/ICMCSI67283.2026.11412777.
P. Vaishali and M. S. Sure Mamatha, “Water quality analysis and prediction using random forest and naive Bayes,” in 16th International Conference on Advances in Computing, Control, and Telecommunication Technologies (ACT), 2025, pp. 7999–8005.
“Swarm-based adaptive control protocol for secure communication and decision making in distributed IoT systems,” in 2026 1st International Electronics & Packaging Technologies Conference: Bridging Skills & Innovation for India’s Industry (EPT India), 2026, DOI: 10.1109/EPDMC67535.2026.11496524.
S. Swapna, K. Santosh, Y. S. Yali, A. Bobonazarov, et al., “Emergent behaviour recognition using reinforcement learning-driven control in complex cybernetic IoT architectures,” in 2026 1st International Electronics & Packaging Technologies Conference (EPDMC), 2026, DOI: 10.1109/EPDMC67535.2026.11496488.
D. Pyari, S. Swapna, R. Gupta, K. Uma Lakshmi, K. Santosh, and B. Kiran Bala, “Computer vision-powered smart attendance and behavior monitoring system for early detection of student disengagement,” in 2026 1st International Conference on Emerging Trends in Advancements and Applications of Computational Intelligence Techniques (ETAACT), 2026, DOI: 10.1109/ETAACT69135.2026.11542033.
M. D. Souza, S. Siddamsetti, A. O. Mulani, S. Subbaiah, V. Kumara, and S. Ramya, “Enhanced detection of breast cancer microcalcifications in MRI via hybrid LOOP and Haralick features with stacked autoencoder classification,” Journal of Robotics and Control, vol. 6, no. 6, 2025, DOI: 10.18196/jrc.v6i6.26502.
K. K. Kamarajugadda, S. Siddamsetti, B. Srilakshmi, K. S. Ranjith, et al., “Hybrid deep ensemble approach using ResNet101 and RFGB for heart disease prediction,” in IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies (INSPECT), Gwalior, India, 2025, DOI: 10.1109/INSPECT67393.2025.11350395.
K. S. Varshini, S. Siddamsetti, H. P. Ramya, M. Ponni Valavan, M. B. Alazzam, and A. B. Rajab, “Federated LSTM framework for secure and robust high-frequency trading prediction,” in 2025 International Conference on Sustainability, Innovation & Technology (ICSIT), 2025, DOI: 10.1109/ICSIT65336.2025.11294658.
K. S. Ranjith, D. N. Reddy, S. Siddamsetti, B. Srilakshmi, K. K. Kamarajugadda, and S. K. Pathuri, “Context-aware deep learning for sarcasm detection in textual data using ResNet-50 and RF+XGBoost,” in 2025 IEEE 3rd International Symposium on Sustainable Energy, Signal Processing and Cybersecurity (iSSSC), 2025, DOI: 10.1109/iSSSC66652.2025.11388086.
K. K. Kamarajugadda, B. Srilakshmi, S. K. Pathuri, K. S. Ranjith, D. N. Reddy, and S. Siddamsetti, “Enhancing cardiovascular disease prediction using YOLO-driven deep feature extraction and RFGB ensemble learning,” in 2025 IEEE 3rd International Symposium on Sustainable Energy, Signal Processing and Cybersecurity (iSSSC), 2025, DOI: 10.1109/iSSSC66652.2025.11388314.
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K. K. Kamarajugadda, B. Srilakshmi, K. S. Ranjith, D. N. Reddy, et al., “Brain stress analysis during sleep using random forest and XGBoost ensemble learning,” in IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies (INSPECT), Gwalior, India, 2025, DOI: 10.1109/INSPECT67393.2025.11350743.
M. D. Souza, N. Kumaresh, S. Siddamsetti, S. Adiga, S. K. Sudhakar, S. K. Sahu, and A. Anandh, “Transfer learning with VGG16 on merged ultrasound datasets for automated breast cancer classification with Grad-CAM interpretability,” Journal of Robotics and Control, vol. 7, no. 1, 2026, DOI: 10.18196/jrc.v7i1.26501.
S. Manimekalai, K. M. Saranya, S. Swapna, M. Govindasamy, G. Nagaraju, and R. Dhaaraani, “MLP-GRU framework for enhanced prediction of cardiovascular disease risk,” in 2026 1st International Conference on Emerging Trends in Advancements and Applications of Computational Intelligence Techniques (ETAACT), 2026, DOI: 10.1109/ETAACT69135.2026.11541383.
D. S. Jadhav, N. R. Wankhade, S. B. Bagal, P. U. Mandlik, A. G. Gaikwad, and A. Kulkarni, “Adaptive pulse-controlled learning in differential 1T1R memristor crossbar for neuromorphic computing,” International Journal of Computer Information Systems & Industrial Management Applications, vol. 18, 2026, DOI: 10.70917/ijcisim-2026-1971.
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T. Hymavathi, R. Srilakshmi, K. B. Rekha, P. A. A. Saleem, and G. Prathyusha, “Sentiment analysis in social media: A machine learning perspective,” in 2025 International Conference on Sustainability, Innovation & Technology (ICSIT), 2025, DOI: 10.1109/ICSIT65336.2025.11295028.
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M. Kusuma Sri, K. Purushotham, K. Sasikala, A. Senthil Kumar, and P. Subbaraju, “Classification of breast histopathology images using color deconvolution and DenseNet architecture,” in 2026 6th International Conference on Pervasive Computing and Social Networking (ICPCSN), Salem, India, 2026, pp. 952–957, DOI: 10.1109/ICPCSN68523.2026.11543706.
O. P. Agrawal, T. Manohara H., S. Swapna, M. Dash, D. Jyothi, and S. Muthuperumal, “Developing a crisis response model using explainable AI and particle swarm optimization for corporate resilience,” in 2026 6th International Conference on Intelligent Technologies (CONIT), 2026, pp. 1–6.
T. Aditya Sai Srinivas, S. Siddamsetti, T. Ranga Babu, S. Agarwal, S. Vijaya Nirmala, and N. M. Jyothi, “An intelligent neural network based framework for enhancing gearbox fault detection and optimal fault recognition in wireless sensor network environments,” Journal of Vibration Engineering & Technologies, vol. 14, no. 7, 2026, DOI: 10.1007/s42417-026-02665-8.
“Edge enabled brain computer interface for cognitive state prediction and real time neuroadaptive cybernetic control,” 2026. [Complete publication metadata could not be reliably verified.]
“Vision-transformer-based AI system for detecting student disengagement in Indian rural smart classrooms,” 2026. [Complete publication metadata could not be reliably verified.]
“Elevating IoT-driven manufacturing with edge computing: Redefining network architectures and empowering AI-driven predictive maintenance with digital twin technology,” 2026. [Complete publication metadata could not be reliably verified.]