Deep Email Analytics: A System for Uncovering Human Values in Unstructured Corporate Communication G Sujith, H A Sudhanva, M P Darshan, N S Girish, P Priyanga, Prashanth Kannadaguli Proceedings of 2025 6th International Conference on Communication Computing and Industry 6 0 C2i6 2025, 2025 The increasing volume in the digital communication within organizations presents a unique opportunity to understand the underlying human values that drive corporate culture and decision-making. However, analyzing vast amounts of unstructured text, such as emails, poses significant difficulties, especially when dealing with the nuanced and often imbalanced representation of different values. This research proposes a sophisticated deep learning platform designed for the value-based analysis of business correspondence. Within our research, we used recurrent neural networks to classify a large dataset of emails into a number of categories. What is novel about our approach is the fact that we tackle the class imbalance problem in our data, meaning that we pay special attention to the values that are observed less frequently. This means our model becomes significantly better at identifying those less frequently observed, highly valuable values. Our completed system is a valuable tool in decoding behavior and ethics at a given organization.
Quantum cryptography-enhanced cyber security intrusion detection system APTs attacks in blockchain Senthil G. A., R. Prabha, P. Priyanga, S. Sridevi Advancing Cyber Security Through Quantum Cryptography, 2024 The novel proposed in this paper aims to revolutionize cybersecurity within Blockchain systems by integrating Quantum Cryptography with federated deep reinforcement learning intrusion detection systems (IDPS). This pioneering fusion of cutting-edge technologies offers a multifaceted defense mechanism against advanced persistent threats (APTs) while preserving the decentralized nature of Blockchain networks. Complementing Quantum Cryptography, federated deep reinforcement learning enhances cybersecurity by deploying AI-driven intrusion detection systems across decentralized Blockchain nodes. This decentralized learning paradigm empowers Blockchain networks to adapt dynamically to evolving cyber threats, ensuring timely and effective responses to malicious activities. Quantum Cryptography and federated deep reinforcement learning, the proposed framework defines strategy against sophisticated cyber-attacks, bolstering the resilience of Blockchain systems. Markov Decision Process is the reinforcement learning algorithm used in the proposed system that detects cyber-attacks and threats.
Application of Artificial Intelligence in Resource-Poor Healthcare P. Priyanga, N. C. Naveen, K. R. Pradeep AI Driven Digital Twin and Industry 4 0 A Conceptual Framework with Applications, 2024 The healthcare environment is driving enterprises towards a new health IT infrastructure strategy. Organizations must invest in new IT that can meet the needs of hospitals and patients as data volumes grow every day. This is required as hospitals demand real-time access to critical diagnostic information that can improve care quality. Centers for Medicare & Medicaid Services have made significant alterations to the way healthcare providers generate, store, and analyze digital data. A large amount of data is now easily available on the Web and is generally heterogeneous and unstructured. Machine Learning (ML) techniques can be applied to automatically retrieve, classify or cluster observations on large data. Big Data (BD) analytics has led to many recent initiatives in both theory and practice and has inspired the interest in the ML community. Developing prediction models for BD problems with ML as a service is poised to change Healthcare Analytics toward a better future and is the next big thing in the healthcare industry as hospitals start to adopt advanced data analytics capabilities. Since BD is associated with high variety, variable, velocity, IoT data, advanced ML techniques are required to generate knowledge that can be used to improve results in the process of delivering patient care.
Strategic Teaching Enhancement through Predictive Analysis for Individuals (STEP.AI) Harsha S, Sreevidya Rampura Chandrappa, Priyanga P, Bhavanishankar K 2024 IEEE International Conference on Teaching Assessment and Learning for Engineering Tale 2024 Proceedings, 2024 In the evolving landscape of educational technology, predictive assessment using learning level classification has emerged as a pivotal tool for enhancing personalized learning experiences. This research paper delves into the methodologies and efficacy of predictive assessment models that classify learners' proficiency levels to forecast their future academic performance. By leveraging machine learning algorithms and extensive educational data, our study develops a robust framework capable of dynamically assessing student capabilities and predicting their learning trajectories. The proposed regression-based model integrates a variety of features including prior academic records, engagement metrics, and cognitive skills assessments to create a comprehensive learning profile for each student. The research findings demonstrate that predictive assessment models can significantly improve the accuracy of proficiency level classification, thus enabling educators to tailor instructional strategies to individual student needs. The implementation of these models in real-world classroom settings shows a marked improvement in student outcomes, as the predictions allow for timely interventions and support. Moreover, this research highlights the potential of predictive assessments to identify at-risk students early, providing a proactive approach to educational support. In conclusion, the integration of predictive assessment and learning level classification represents a transformative approach in education, promising enhanced educational experiences and outcomes through data-driven insights. Future work will focus on refining these models to accommodate diverse learning environments and further validating their effectiveness across different educational contexts.
The Intersection of Art and AI: Innovations in Creative Collaboration Shamanth N, Sagar T R, Sanjana Ballal, Khushi Etagi, Priyanga P 2nd IEEE International Conference on Iot Communication and Automation Technology Icicat 2024, 2024 The combination of artificial intelligence (AI) and art has revolutionized creative collaboration, pushing the boundaries of traditional artistic practices. This paper investigates the transformative impact of AI on digital art, emphasizing its dual role as both a creative tool and a collaborator. We explore advanced methodologies, including Neural Style Transfer (NST) and Generative Adversarial Network (GAN), to illustrate how thesetechnologies facilitate innovative artistic expressions. Our approach involves collecting a diverse dataset of artistic works, which undergoes augmentation through techniques like Deep Dream. We utilize Convolutional Neural Network(CNN) for feature extraction and employ NST to apply various artistic styles to AI-generated images. The results demonstrate that AI not only enhances the creative process but also fosters collaboration between artists and machines, leading to unique and diverse outputs. By addressing ethicalissues and integrating user feedback, this study highlights the potential of AI to reshape the future of art and collaboration, ultimately expanding the horizons of creativity.
Advancing Medical Imaging: A Focus on Efficient Net for Brain Tumor Classification R Shashidhar, R Manasa, K M Megha, P Priyanga, A S Manjunath, M Roopa 2nd IEEE International Conference on Networks Multimedia and Information Technology Nmitcon 2024, 2024 In the field of medical diagnosis, the increasing occurrence of brain tumors calls for creative approaches to detect and intervene at early stages. This proposed work focus on the Efficient Net (EffNet) architecture to address the challenges associated with brain tumour recognition and sorting from Magnetic Resonance Imaging (MRI) scans. The main objectives are outlined to develop a robust model capable of discriminating between various tumour types and normal brain tissues. Four distinct classes—glioma tumour, pituitary tumour, meningioma tumour, and no tumour compose the dataset, providing diversity for training and evaluation. Using projected method got the accuracy of 98.08% accuracy. Our proposed method detects the all the four classes effectively.
Review on Event Extraction for BioNLP with a Survey Veena V Pattankar, P Priyanga 2023 International Conference for Advancement in Technology Iconat 2023, 2023 The scientific literature contains essential information connected to proteins, drugs, and symptoms. Researchers are extracting organized, brief, and understandable information from unstructured texts to keep up with the growing number of publications. Within the context of the biomedical area, this study investigates event extraction and natural language comprehension. We will build a flexible description of an event by first outlining several terminological methods. Second, we demonstrate event extraction, its challenges, and annotated corpora. Third, we investigate representative methodologies and provide an up-to-date analysis with performance discussion. We provide a taxonomy to assist academics in navigating the deluge of event extraction works. Fourth, we compare biomedical solutions to those in other domains to uncover fresh research opportunities and approaches. Finally, we discuss potential applications, explainability, and knowledge injection.
The Smart Factory of Tomorrow: Artificial Intelligence and Machine Learning Reshaping Manufacturing Processes Priyanga P, S. Sridevi, Ashwini K, Deepa S R 2023 2nd International Conference on Smart Technologies for Smart Nation Smarttechcon 2023, 2023 The smart factory of the future would not be possible without the development of AI and ML technologies, which have ushered in a new era of production. Traditional industrial processes are being revolutionized by AI and ML due to their capacity to evaluate large quantities of data and make autonomous choices, which is leading to greater efficiency, productivity, and profitability. Predictive maintenance is one area where AI and ML are making important contributions. These systems may prevent unexpected and expensive failures by constantly monitoring equipment performance and analyzing real-time data. Taking preventative measures like these results in less downtime, lower maintenance expenses, and more efficient machinery. Synergies between AI and ML are improving factory quality assurance. These technologies can identify even the smallest flaws or deviations from product standards using sophisticated vision systems and pattern recognition algorithms. Manufacturing companies may reduce waste and customer complaints by maintaining a constant quality standard via the use of automated inspection methods. Optimization of production planning and scheduling is another important use of AI and ML in manufacturing.
Learning Engagement and Adaptation Platform (LEAP): An LSTM-Based Approach Leveraging VARK Learning Preferences S Harsha, SR Chandrappa, K Bhavanishankar, P Priyanga 2025 IEEE International Conference on Teaching, Assessment, and Learning for … , 2025 2025
Approach for Improved Classification VV Pattankar, P Priyanga Computer Vision and Robotics: Proceedings of CVR 2024, 385 , 2025 2025
Early Detection of Diabetic Retinopathy Using Transfer Learning with VGG16: A Deep Learning Approach for Retinal Fundus Analysis P P Journal of Neonatal Surgery 14 (22s), 651-660 , 2025 2025
Hydroponics: Innovative Sustainable Technologies for Tomato Cultivation J Bhagyashree Ambore, Priyanga P, Veena V Pattankar, Nivedita G Y, Sunitha K Journal of Information Systems Engineering and Management 10 (53s), 553-563 , 2025 2025
Hydroponics: Advancing Sustainable Technologies and Applications in Crop Production with a Focus on Lettuce Cultivation AG Bhagyashree Ambore , Smitha B A, Sunitha K, Priyanga P Journal of Information Systems Engineering and Management 10 (5s), 636-651 , 2025 2025 Citations: 3
Strategic Teaching Enhancement through Predictive Analysis for Individuals (STEP. AI) S Harsha, SR Chandrappa, P Priyanga, K Bhavanishankar 2024 IEEE International Conference on Teaching, Assessment and Learning for … , 2024 2024 Citations: 4
Application of Artificial Intelligence in Resource-Poor Healthcare P Priyanga, NC Naveen, KR Pradeep AI-Driven Digital Twin and Industry 4.0, 156-167 , 2024 2024
Enhancing Biomedical Event Extraction with Error Data Detection: A Novel Approach for Improved Classification Performance VV Pattankar, P Priyanga International Conference on Computer Vision and Robotics, 385-395 , 2024 2024 Citations: 1
Role of AI and Machine Learning AS Manek, P Priyanga, S Christa, N Dawda Data Science and Big Data Analytics: Proceedings of IDBA 2023, 33 , 2024 2024
The Intersection of Art and AI: Innovations in Creative Collaboration D Priyanga P 2nd International Conference on IoT, Communication & Automation Technology … , 2024 2024 Citations: 1
Quantum Cryptography-Enhanced Cyber Security Intrusion Detection System APTs Attacks in Blockchain SS Senthil G. A,R. Prabha, P. Priyanga Advancing Cyber Security Through Quantum Cryptography, 87 to 102 , 2024 2024 Citations: 8
Advancing Medical Imaging: A Focus on Efficient Net for Brain Tumor Classification PP Shashidhar R 2024 Second International Conference on Networks, Multimedia and Information … , 2024 2024 Citations: 5
Anti-money Laundering Analytics on the Bitcoin Transactions P P Springer Nature (LNEE) 1075 , 2023 2023
The Smart factory of tomorrow: Artificial intelligence and machine learning reshaping manufacturing processes P Priyanga, S Sridevi, K Ashwini, SR Deepa 2023 Second International Conference On Smart Technologies For Smart Nation … , 2023 2023 Citations: 6
Role of AI and Machine Learning in Mental Healthcare AS Manek, P Priyanga, S Christa, N Dawda International Conference on Data Science and Big Data Analysis, 33-48 , 2023 2023
Review on event extraction for BioNLP with a survey VV Pattankar, P Priyanga 2023 International Conference for Advancement in Technology (ICONAT), 1-5 , 2023 2023 Citations: 2
A Machine Learning Approach for the Detection of QRS Complexes in Electrocardiogram (ECG) Using Discrete Wavelet Transform (DWT) Algorithm SHSAH Ali Rizwan ,P Priyanga,Emad H. Abualsauod ,Syed Nasrullah Zafrullah Computational Intelligence and Neuro Science 2022 (9023478), 8 , 2022 2022 Citations: 48
“Prediction of Chronic Kidney Disease using Fine Tune Based SVM in Internet of Things” Dr. Jyothi, Dr. Priyanga P Neuro Quantology 20 (Issue 15), 7430-7443 , 2022 2022
A hybrid recurrent neural network‐logistic chaos‐based whale optimization framework for heart disease prediction with electronic health records P Priyanga, VV Pattankar, S Sridevi Computational Intelligence, 1-29 , 2020 2020 Citations: 50
An Efficient Cluster Based Deep Neural Network (C-DNN) for Detection of Heart Disease P Priyanga International Journal of Advanced Science and Technology 29 (No. 5, (2020 … , 2020 2020 Citations: 2
MOST CITED SCHOLAR PUBLICATIONS
A hybrid recurrent neural network‐logistic chaos‐based whale optimization framework for heart disease prediction with electronic health records P Priyanga, VV Pattankar, S Sridevi Computational Intelligence, 1-29 , 2020 2020 Citations: 50
A Machine Learning Approach for the Detection of QRS Complexes in Electrocardiogram (ECG) Using Discrete Wavelet Transform (DWT) Algorithm SHSAH Ali Rizwan ,P Priyanga,Emad H. Abualsauod ,Syed Nasrullah Zafrullah Computational Intelligence and Neuro Science 2022 (9023478), 8 , 2022 2022 Citations: 48
Web analytics support system for prediction of heart disease using naive bayes weighted approach (nbwa) P Priyanga, NC Naveen 2017 Asia modelling symposium (AMS), 21-26 , 2017 2017 Citations: 16
Quantum Cryptography-Enhanced Cyber Security Intrusion Detection System APTs Attacks in Blockchain SS Senthil G. A,R. Prabha, P. Priyanga Advancing Cyber Security Through Quantum Cryptography, 87 to 102 , 2024 2024 Citations: 8
Analysis of Machine Learning Algorithms in Health Care to Predict Heart Disease P Priyanga, NC Naveen International Journal of Healthcare Information Systems and Informatics … , 2018 2018 Citations: 8
The Smart factory of tomorrow: Artificial intelligence and machine learning reshaping manufacturing processes P Priyanga, S Sridevi, K Ashwini, SR Deepa 2023 Second International Conference On Smart Technologies For Smart Nation … , 2023 2023 Citations: 6
Advancing Medical Imaging: A Focus on Efficient Net for Brain Tumor Classification PP Shashidhar R 2024 Second International Conference on Networks, Multimedia and Information … , 2024 2024 Citations: 5
Strategic Teaching Enhancement through Predictive Analysis for Individuals (STEP. AI) S Harsha, SR Chandrappa, P Priyanga, K Bhavanishankar 2024 IEEE International Conference on Teaching, Assessment and Learning for … , 2024 2024 Citations: 4
Hydroponics: Advancing Sustainable Technologies and Applications in Crop Production with a Focus on Lettuce Cultivation AG Bhagyashree Ambore , Smitha B A, Sunitha K, Priyanga P Journal of Information Systems Engineering and Management 10 (5s), 636-651 , 2025 2025 Citations: 3
Review on event extraction for BioNLP with a survey VV Pattankar, P Priyanga 2023 International Conference for Advancement in Technology (ICONAT), 1-5 , 2023 2023 Citations: 2
An Efficient Cluster Based Deep Neural Network (C-DNN) for Detection of Heart Disease P Priyanga International Journal of Advanced Science and Technology 29 (No. 5, (2020 … , 2020 2020 Citations: 2
Enhancing Biomedical Event Extraction with Error Data Detection: A Novel Approach for Improved Classification Performance VV Pattankar, P Priyanga International Conference on Computer Vision and Robotics, 385-395 , 2024 2024 Citations: 1
The Intersection of Art and AI: Innovations in Creative Collaboration D Priyanga P 2nd International Conference on IoT, Communication & Automation Technology … , 2024 2024 Citations: 1
LITERATURE REVIEW: WEB MINING TECHNIQUES IN HEALTH CARE APPLICATIONS P Priyanga, NC Naveen International journal of Computer Engineering and Applications, 122-133 , 2016 2016 Citations: 1
Mining Health Data using Weighted Approach P Priyanga, NC Naveen Communications on Applied Electronics 5 (10), 1-6 , 2016 2016 Citations: 1
Learning Engagement and Adaptation Platform (LEAP): An LSTM-Based Approach Leveraging VARK Learning Preferences S Harsha, SR Chandrappa, K Bhavanishankar, P Priyanga 2025 IEEE International Conference on Teaching, Assessment, and Learning for … , 2025 2025
Approach for Improved Classification VV Pattankar, P Priyanga Computer Vision and Robotics: Proceedings of CVR 2024, 385 , 2025 2025
Early Detection of Diabetic Retinopathy Using Transfer Learning with VGG16: A Deep Learning Approach for Retinal Fundus Analysis P P Journal of Neonatal Surgery 14 (22s), 651-660 , 2025 2025
Hydroponics: Innovative Sustainable Technologies for Tomato Cultivation J Bhagyashree Ambore, Priyanga P, Veena V Pattankar, Nivedita G Y, Sunitha K Journal of Information Systems Engineering and Management 10 (53s), 553-563 , 2025 2025
Application of Artificial Intelligence in Resource-Poor Healthcare P Priyanga, NC Naveen, KR Pradeep AI-Driven Digital Twin and Industry 4.0, 156-167 , 2024 2024
