Rajkumar Kyadasu Research Profile

About

Innovative and results-driven Lead Data Engineer with extensive experience in developing, automating, and optimizing mission-critical applications and deployments. Proven expertise in setting up Databricks clusters and providing ETL solutions for data extraction, transformation, and loading (ETL), leading to improved system efficiencies and cost reductions. Skilled in implementing DevOps practices to ensure continuous delivery and high software quality, leveraging advanced configuration management tools, continuous integration, and cloud services (AWS and Azure). Expert in designing, developing, and migrating Java and Python applications to private Azure Cloud networks, automating entire infrastructures to support strategic business objectives.

Skills

Experience

Lead Big Data Engineer

AT&T Inc

Apr-2021 to Present

Education

Rivier University

Master of Science (MS) in Computer Science

Passout Year: 2013

Jawaharlal Nehru Technological University, Hyderab...

BTech in Computer Science

Passout Year: 2009

Peer-Reviewed Articles

Internal and External Re-keying and the way forward

Side Channel Analysis are the security attacks due to the issues in the implementations. This attack bypasses the mathematical security provided by the cryptographic algorithms. These attacks are broadly categorized into the issues related to architectural of the chip manufacturing, attack due to unwanted leakages like power leakage, acoustic leakage, thermal leakage or electromagnetic leakages, and the issues due to programming vulnerabilities for example the heartbleed bug etc. The architectural related issues are fixed when the newer version of hardware is designed once the vulnerability is found in the earlier version. The programming related attacks are solved by patching the software and updating the code that caused the vulnerability to be exploited. The leakage issues are the ongoing issues since it was first discovered in 1997. Among the various leakage issues, the acoustic and thermal leakages aids in the attack related to power analysis. The Electromagnetic attack boils down to the power analysis issue and hence, it all comes down to the power analysis attack. Since it was discovered, the researchers have suggested the solutions for them but on the other side, they would also be vulnerable again. The Power analysis attacks are mainly classified into Simple Power Analysis (SPA), Differential Power Analysis (DPA), Correlation Power Analysis (CPA), and profiled attacks. Their countermeasures are mainly masking and rekeying apart from architectural changes. The masking has been researched extensively and have been widely implemented countermeasure. However, it comes with a very big overhead. Therefore, the researchers started exploring the rekeying to counter them. Rekeying has been classified mainly into the internal and external rekeying both having its advantages and disadvantages. There is currently no literature available that discusses both in detail. This work surveys the work on both the approaches and suggest the way forward for the researchers of the re-keying.

ASD-Pipeline: An Ensemble Machine Learning Framework Integrating Feature Selection, Behavioural Clustering, and Class Rebalancing for Accurate Autism ...

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by a variety of behavioral and cognitive patterns. Early and precise detection is critical in enabling timely interventions. Conventional classification models frequently exhibit poor generalization due to irrelevant features, unstructured behavioral data, and severe class imbalance. Despite current advances in machine learning for ASD detection, current models do not integrate adaptive feature selection, behavioral grouping, or imbalanced class handling in a unified, end-to-end pipeline. The lack of incorporation frequently results in suboptimal performance and limited interpretability. This study proposes a new ensemble-based framework called ASD-Pipeline, which integrates flexible feature selection, hybrid clustering, synthetic minority oversampling, and ensemble voting classification to improve the predictive performance for ASD identification. The proposed ASD-Pipeline framework uses a five-stage process to improve the accuracy of autism spectrum disorder prediction. First, the dataset is normalized utilizing Min-Max scaling to guarantee that the feature ranges remain consistent. Next, feature selection is performed utilizing FlexiFeat, an ensemble method integrating filter-based (CfsSubsetEval with BestFirst), wrapper-based (WrapperSubsetEval with GreedyStepwise), and embedded (ReliefF with Ranker) techniques to maintain only the most pertinent feature. The ClusterGroup stage uses K-Means clustering (k=5) and DBSCAN improvement (ε=0.5, minPts=3) within each cluster to create behavioral groups and remove outliers. The ReBalance stage uses Cluster-SMOTE to tackle class imbalance by producing synthetic samples for the minority class and a balanced dataset. Finally, the ASDClassifier stage involves training an ensemble of Logistic Regression, Support Vector Machine, and Gradient Boosting classifiers that are combined using soft voting. Metrics used to assess the model include accuracy, precision, recall, F1-score, and Matthews Correlation Coefficient (MCC). The proposed ASD-Pipeline surpassed existing models, achieving a significantly higher accuracy of 96.18% compared to previous techniques ranging from 76.80% to 90.60%. It also scored 91.51% precision, 91.63% recall, 95.57% F1-score, and 92.51% specificity. These findings emphasize the pipeline's efficacy in enhancing generalization and tackling difficulties such as feature relevance, behavioral grouping, and class imbalance for ASD prediction. The ASD-Pipeline offers a reliable, interpretable, and modular machine learning solution for ASD prediction. Its incorporated method tackles critical challenges in feature relevance, behavioral variability, and data imbalance, rendering it a promising tool for healthcare practitioners and researchers seeking data-driven insights into early ASD detection.

Scholar9 Profile ID

S9-112024-1206390

Publication

(0)

Article Reviewed

(32)

Citations

(0)

Network

(0)

Conferences/Seminar

(0)

Mr. Rajkumar Kyadasu