Ascertaining Susceptibilities in Smart Contracts: A Quantum Machine Learning Approach Amulyashree Sridhar, Kalyan Nagaraj, Shambhavi Bangalore Ravi, Sindhu Kurup Entropy, 2025 The current research aims to discover applications of QML approaches in realizing liabilities within smart contracts. These contracts are essential commodities of the blockchain interface and are also decisive in developing decentralized products. But liabilities in smart contracts could result in unfamiliar system failures. Presently, static detection tools are utilized to discover accountabilities. However, they could result in instances of false narratives due to their dependency on predefined rules. In addition, these policies can often be superseded, failing to generalize on new contracts. The detection of liabilities with ML approaches, correspondingly, has certain limitations with contract size due to storage and performance issues. Nevertheless, employing QML approaches could be beneficial as they do not necessitate any preconceived rules. They often learn from data attributes during the training process and are employed as alternatives to ML approaches in terms of storage and performance. The present study employs four QML approaches, namely, QNN, QSVM, VQC, and QRF, for discovering susceptibilities. Experimentation revealed that the QNN model surpasses other approaches in detecting liabilities, with a performance accuracy of 82.43%. To further validate its feasibility and performance, the model was assessed on a several-partition test dataset, i.e., SolidiFI data, and the outcomes remained consistent. Additionally, the performance of the model was statistically validated using McNemar’s test.
Aurora: A Multi-Personality AI Voice Assistant for Domain-Specific and Emotion-Aware Interactions Bommireddy Neha, G Sri Sai Meghana, Meet Jain, Kalyan Nagaraj 2025 9th International Conference on Computational System and Information Technology for Sustainable Solutions Csitss 2025, 2025 Voice assistants have become essential tools for everyday tasks, yet their inability to provide domain-specific expertise, emotional awareness, and personalized interactions limits their effectiveness in specialized contexts such as mental health, finance, and education. This paper introduces Aurora, a next-generation AI voice assistant designed to address these gaps through a multi-personality architecture. Aurora integrates distinct personas such as a therapist, financial advisor, doctor and teacher-each with tailored knowledge domains, tones, and behaviors, enabling dynamic personality switching based on user's queries. The system employs advanced text-to-speech synthesis to adapt tone, pitch, and speaking style appropriate to each persona and emotional context, while a toxicity detection module ensures safe and ethical interactions by filtering inappropriate inputs. Built with technologies like Whisper for speech recognition, Eleven labs for expressive voice synthesis, and a session-based memory for contextual recall, Aurora delivers natural, human-like conversations. Preliminary evaluations highlight its potential to enhance user engagement and trust in specialized domains, paving the way for more intelligent, adaptive, and responsible voice technologies.
An Intelligent Multi-Module Financial Assistant for Personalized Budgeting, Risk Alerts, and Adaptive Investment Advisory Pavithra Ganta, Gourav Agarwal, Rajshekhar Jha, Kalyan Nagaraj 2025 9th International Conference on Computational System and Information Technology for Sustainable Solutions Csitss 2025, 2025 Managing personal finances in today's fragmented fintech landscape presents significant challenges due to the absence of integrated, user-aware systems. This work proposes a unified, intelligent finance management framework that automates budgeting, credit recommendation, risk detection, and interactive advisory services. The system combines modular components-namely, a fraud detection engine using XGBoost, a personalized credit card ranker powered by learning-to-rank models, and a finance chatbot built on LLM with intent and context detection pipelines. Key innovations include categoryaware document embeddings, semantic user profiling, and rulebased behavior mapping for spending trends. Evaluation results demonstrate effective fraud classification, accurate credit matching, and scalable document-grounded advisory generation. The platform is designed for young professionals, freelancers, and digital users, and emphasizes scalability, explainability, and contextual intelligence across financial tasks.
Envisaging prominence of indian telecom operators using an ensemble link based approach Amulyashree Sridhar, Sharvani GS, Manjunatha Reddy AH, Kalyan Nagaraj Indian Journal of Computer Science and Engineering, 2020 Understanding ambiences from consumers helps in inspecting market value of an artifact. Data and voice quality of telecom service are often praised and targeted in social networking platforms. There is a need to analyze these opinions to infer renown of service providers. In this context, data is extracted for major Indian telecom service providers from Twitter and FaceBook. Furthermore, performance metrics of these providers are collected from TRAI portal to compare projected thresholds with customer opinions. These datasets are retrieved in predefined timeframe to ensure that information isn’t biased. Towards exploring a prevalent service provider, an ensemble node ranking approach is designed based on ideologies of SVM and RNN algorithms (SVMRNNRank). This approach is modelled based connectivity in the network. Its effectiveness is demonstrated by calculating certain statistical metrics. Comparative analysis reflects that influential nodes identified from SVMRNNRank have better acceptance amongst social network users and TRAI performance indices.
The eminence of co-expressed ties in schizophrenia network communities Amulyashree Sridhar, Sharvani GS, AH Manjunatha Reddy, Biplab Bhattacharjee, Kalyan Nagaraj Data, 2019 Exploring gene networks is crucial for identifying significant biological interactions occurring in a disease condition. These interactions can be acknowledged by modeling the tie structure of networks. Such tie orientations are often detected within embedded community structures. However, most of the prevailing community detection modules are intended to capture information from nodes and its attributes, usually ignoring the ties. In this study, a modularity maximization algorithm is proposed based on nonlinear representation of local tangent space alignment (LTSA). Initially, the tangent coordinates are computed locally to identify k-nearest neighbors across the genes. These local neighbors are further optimized by generating a nonlinear network embedding function for detecting gene communities based on eigenvector decomposition. Experimental results suggest that this algorithm detects gene modules with a better modularity index of 0.9256, compared to other traditional community detection algorithms. Furthermore, co-expressed genes across these communities are identified by discovering the characteristic tie structures. These detected ties are known to have substantial biological influence in the progression of schizophrenia, thereby signifying the influence of tie patterns in biological networks. This technique can be extended logically on other diseases networks for detecting substantial gene “hotspots”.
Biosociolink: A decision support system for analyzing link properties in biological and social networks Amulyashree Sridhar, , Sharvani GS, Ramakanth Kumar P, AH Manjunatha Reddy, Kalyan Nagaraj, , , , and International Journal of Engineering and Advanced Technology, 2019 Network based data representation has received widespread attention over the years. Data is oriented in graph format by aligning information as nodes and edges. Some of the predominant network cases include biological and social sciences. There is a growing need to extract knowledge patterns from network orientations. In such scenario, the current study focuses on extracting data patterns from network data. Schizophrenia gene data and TRAI wireless performance data is identified for performing biological and social network analysis. Biological network analysis is performed to identify relevant gene ties which act as hotspots for identifying disease causing genes. On similar lines, social network analysis is performed on wireless dataset to identify essential telecom operators responsible for customer retention. Based on these outcomes, a decision support system, Bio Socio Link is designed in R programming language to perform biological and social network analysis. The support system accurately detects knowledge patterns from both the datasets. The study is concluded by deploying the support system in local programming environment.
Encrypting and preserving sensitive attributes in customer churn data using novel dragonfly based pseudonymizer approach Kalyan Nagaraj, Sharvani GS, Amulyashree Sridhar Information Switzerland, 2019 With miscellaneous information accessible in public depositories, consumer data is the knowledgebase for anticipating client preferences. For instance, subscriber details are inspected in telecommunication sector to ascertain growth, customer engagement and imminent opportunity for advancement of services. Amongst such parameters, churn rate is substantial to scrutinize migrating consumers. However, predicting churn is often accustomed with prevalent risk of invading sensitive information from subscribers. Henceforth, it is worth safeguarding subtle details prior to customer-churn assessment. A dual approach is adopted based on dragonfly and pseudonymizer algorithms to secure lucidity of customer data. This twofold approach ensures sensitive attributes are protected prior to churn analysis. Exactitude of this method is investigated by comparing performances of conventional privacy preserving models against the current model. Furthermore, churn detection is substantiated prior and post data preservation for detecting information loss. It was found that the privacy based feature selection method secured sensitive attributes effectively as compared to traditional approaches. Moreover, information loss estimated prior and post security concealment identified random forest classifier as superlative churn detection model with enhanced accuracy of 94.3% and minimal data forfeiture of 0.32%. Likewise, this approach can be adopted in several domains to shield vulnerable information prior to data modeling.
Biomedical Signal Synthesis and Analysis Using Multimodal Deep Learning Frameworks KP Pramath, B Mohan, K Nagaraj, BR Shambhavi 2025 IEEE 9th International Conference on Information and Communication … , 2025 2025.0
An Intelligent Multi-Module Financial Assistant for Personalized Budgeting, Risk Alerts, and Adaptive Investment Advisory P Ganta, G Agarwal, R Jha, K Nagaraj IEEE Xplore , 2025 2025.0
Aurora: A Multi-Personality AI Voice Assistant for Domain-Specific and Emotion-Aware Interactions B Neha, GSS Meghana, M Jain, K Nagaraj 2025 9th International Conference on Computational System and Information … , 2025 2025.0 Citations: 1
Synthesis, Characterization, Electrochemical Sensing, DFT Analysis, Molecular Docking and Anticancer Activity of a Novel Thiadiazole-Based Azo Dye Incorporating a Barbituric … S Harisha, K Nagaraj, A Sridhar, SRK Kumar, Y Surendranaik Journal of Molecular Structure 1341, 142576 , 2025 2025.0 Citations: 2
Ascertaining Susceptibilities in Smart Contracts: A Quantum Machine Learning Approach A Sridhar, K Nagaraj, S Bangalore Ravi, S Kurup Entropy 27 (9), 933 , 2025 2025.0 Citations: 1
A Novel Machine Learning Ensemble Algorithm to Predict Occurrence K Nagaraj, HS Prashanth, A Sridhar Fifth Congress on Intelligent Systems: CIS 2024, Volume 1 1, 121 , 2025 2025.0
SleepPred: A Machine Learning Tool to Predict Quality of Sleep A Sridhar, K Nagaraj International Conference on Data-Processing and Networking, 689-704 , 2024 2024.0
A Novel Machine Learning Ensemble Algorithm to Predict Occurrence of Cancer K Nagaraj, HS Prashanth, A Sridhar Congress on Intelligent Systems, 121-136 , 2024 2024.0
ENVISAGING PROMINENCE OF INDIAN TELECOM OPERATORS USING AN ENSEMBLE LINK BASED APPROACH A Sridhar, GS Sharvani, AHM Reddy, K Nagaraj Indian Journal of Computer Science and Engineering 11 (3), 297-310 , 2020 2020.0 Citations: 5
The Eminence of Co-Expressed Ties in Schizophrenia Network Communities A Sridhar, S Gs, AHM Reddy, B Bhattacharjee, K Nagaraj Data 4 (4), 149 , 2019 2019.0
Encrypting and preserving sensitive attributes in customer churn data using novel dragonfly based pseudonymizer approach K Nagaraj, S GS, A Sridhar Information 10 (9), 274 , 2019 2019.0 Citations: 9
Detection of phishing websites using a novel twofold ensemble model K Nagaraj, B Bhattacharjee, A Sridhar, S GS Journal of Systems and Information Technology 20 (3), 321-357 , 2018 2018.0 Citations: 49
Emerging trend of big data analytics in bioinformatics: a literature review AS Kalyan Nagaraj, G.S. Sharvani International Journal of Bioinformatics Research and Applications 14 (1/2 … , 2018 2018.0 Citations: 38
Identification of network communities and assessment of privacy using hybrid algorithm K Nagaraj, A Sridhar, GS Sharvani 2017 2nd International Conference on Computational Systems and Information … , 2017 2017.0 Citations: 2
NeuroSVM: a graphical user interface for identification of liver patients K Nagaraj, A Sridhar arXiv preprint arXiv:1502.05534 , 2015 2015.0 Citations: 23
A predictive system for detection of bankruptcy using machine learning techniques K Nagaraj, A Sridhar arXiv preprint arXiv:1502.03601 , 2015 2015.0 Citations: 34
Aggregate Diversity Improvement in Recommender System through Ranking Approach BJ Doddegowda, S Kumar, S Rabindranath, K Nagaraj
Enhanced Electrochemical Response and Promising Anti-Cancer Potential of a Novel Azo Dye: From Sensor Technology to Bcl-Xl Inhibition K Nagaraj, M Reddy R, SR Kumar, N Basavegowda
Journal of Systems and Information Technology K Nagaraj, B Bhattacharjee, A Sridhar, S GS
MOST CITED SCHOLAR PUBLICATIONS
Detection of phishing websites using a novel twofold ensemble model K Nagaraj, B Bhattacharjee, A Sridhar, S GS Journal of Systems and Information Technology 20 (3), 321-357 , 2018 2018.0 Citations: 49
Emerging trend of big data analytics in bioinformatics: a literature review AS Kalyan Nagaraj, G.S. Sharvani International Journal of Bioinformatics Research and Applications 14 (1/2 … , 2018 2018.0 Citations: 38
A predictive system for detection of bankruptcy using machine learning techniques K Nagaraj, A Sridhar arXiv preprint arXiv:1502.03601 , 2015 2015.0 Citations: 34
NeuroSVM: a graphical user interface for identification of liver patients K Nagaraj, A Sridhar arXiv preprint arXiv:1502.05534 , 2015 2015.0 Citations: 23
Encrypting and preserving sensitive attributes in customer churn data using novel dragonfly based pseudonymizer approach K Nagaraj, S GS, A Sridhar Information 10 (9), 274 , 2019 2019.0 Citations: 9
ENVISAGING PROMINENCE OF INDIAN TELECOM OPERATORS USING AN ENSEMBLE LINK BASED APPROACH A Sridhar, GS Sharvani, AHM Reddy, K Nagaraj Indian Journal of Computer Science and Engineering 11 (3), 297-310 , 2020 2020.0 Citations: 5
Synthesis, Characterization, Electrochemical Sensing, DFT Analysis, Molecular Docking and Anticancer Activity of a Novel Thiadiazole-Based Azo Dye Incorporating a Barbituric … S Harisha, K Nagaraj, A Sridhar, SRK Kumar, Y Surendranaik Journal of Molecular Structure 1341, 142576 , 2025 2025.0 Citations: 2
Identification of network communities and assessment of privacy using hybrid algorithm K Nagaraj, A Sridhar, GS Sharvani 2017 2nd International Conference on Computational Systems and Information … , 2017 2017.0 Citations: 2
Aurora: A Multi-Personality AI Voice Assistant for Domain-Specific and Emotion-Aware Interactions B Neha, GSS Meghana, M Jain, K Nagaraj 2025 9th International Conference on Computational System and Information … , 2025 2025.0 Citations: 1
Ascertaining Susceptibilities in Smart Contracts: A Quantum Machine Learning Approach A Sridhar, K Nagaraj, S Bangalore Ravi, S Kurup Entropy 27 (9), 933 , 2025 2025.0 Citations: 1
Biomedical Signal Synthesis and Analysis Using Multimodal Deep Learning Frameworks KP Pramath, B Mohan, K Nagaraj, BR Shambhavi 2025 IEEE 9th International Conference on Information and Communication … , 2025 2025.0
An Intelligent Multi-Module Financial Assistant for Personalized Budgeting, Risk Alerts, and Adaptive Investment Advisory P Ganta, G Agarwal, R Jha, K Nagaraj IEEE Xplore , 2025 2025.0
A Novel Machine Learning Ensemble Algorithm to Predict Occurrence K Nagaraj, HS Prashanth, A Sridhar Fifth Congress on Intelligent Systems: CIS 2024, Volume 1 1, 121 , 2025 2025.0
SleepPred: A Machine Learning Tool to Predict Quality of Sleep A Sridhar, K Nagaraj International Conference on Data-Processing and Networking, 689-704 , 2024 2024.0
A Novel Machine Learning Ensemble Algorithm to Predict Occurrence of Cancer K Nagaraj, HS Prashanth, A Sridhar Congress on Intelligent Systems, 121-136 , 2024 2024.0
The Eminence of Co-Expressed Ties in Schizophrenia Network Communities A Sridhar, S Gs, AHM Reddy, B Bhattacharjee, K Nagaraj Data 4 (4), 149 , 2019 2019.0
Aggregate Diversity Improvement in Recommender System through Ranking Approach BJ Doddegowda, S Kumar, S Rabindranath, K Nagaraj
Enhanced Electrochemical Response and Promising Anti-Cancer Potential of a Novel Azo Dye: From Sensor Technology to Bcl-Xl Inhibition K Nagaraj, M Reddy R, SR Kumar, N Basavegowda
Journal of Systems and Information Technology K Nagaraj, B Bhattacharjee, A Sridhar, S GS
