Publications, Research, and Certifications

A collection of my professional credentials, ongoing research, and published work.

Publications

Analysis of Random Forest, CNN, and Bidirectional LSTM for Intrusion Detection on the NSL-KDD Dataset

This paper presents a comparative study of Random Forest, 1D CNN, and BiLSTM models for intrusion detection using the NSL-KDD dataset. With a unified preprocessing pipeline and focus on challenging attack classes such as R2L and U2R, the study highlights the strengths of each model and demonstrates that Random Forest provides the most consistent and reliable performance.

Conference Name: IDSCS 2025

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Exploring MobileNetV2 and SqueezeNet for Efficient Fruit Freshness Detection in Agricultural Systems

This paper explores the use of MobileNetV2 and SqueezeNet for classifying fruit freshness in agricultural systems. By leveraging lightweight CNN architectures and Grad-CAM for interpretability, the study achieves highly efficient and accurate classification suitable for real-time deployment.

Conference Name: ICATEST 2025

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A Comparative Study of Filter, Wrapper and Embedded Feature Selection Techniques for Intrusion Detection on the NSL-KDD Dataset.

This paper presents a systematic study on intrusion detection using the NSL-KDD dataset, comparing filter, wrapper, and embedded feature selection methods with XGBoost as the primary classifier. The results show that effective feature selection and class balancing techniques significantly improve model performance and overall detection capability.

Conference Name: IJCACI 2025

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A Hybrid Approach for Intrusion Detection Using Feature Selection and Class Balancing on the NSL-KDD Dataset

This paper provides a comparative evaluation of feature selection and class balancing strategies on the NSL-KDD dataset using Random Forest and XGBoost. The findings highlight that combining feature selection with balanced sampling leads to stronger detection of minority attack classes and more reliable overall intrusion detection performance.

Conference Name: CIS 2025

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Research Work

Hybrid Post Quantum Cryptography Cryptosystems: ML-KEM (Kyber) Key Establishment with AES-256 AEAD

This paper proposes a hybrid post-quantum cryptographic framework combining lattice-based key encapsulation (ML-KEM/Kyber) for key exchange with AES-256 AEAD for bulk encryption. It outlines a TLS-style handshake, transcript-bound key derivation using HKDF, and strict nonce discipline for security. The hybrid design maintains AES throughput while adding post-quantum protection against “store-now, decrypt-later” threats. Evaluations confirm minimal handshake overhead and near-baseline performance, aligning with NIST PQC transition guidance.

College Research Work - Cryptography and Network Security

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FinBERT-Driven Hybrid Deep Learning Models for Financial Forecasting: A Comparative Evaluation of LSTM, CNN, and XGBoost

This research presents a hybrid deep learning framework integrating FinBERT-based sentiment embeddings with quantitative stock indicators to predict financial market movements. It compares FinBERT-LSTM, FinBERT-CNN, and FinBERT-XGBoost architectures across multiple performance metrics, demonstrating that sentiment-driven features significantly enhance predictive accuracy. The FinBERT-XGBoost model achieved ~90% accuracy in sentiment classification and outperformed traditional numeric-only methods, validating the role of sentiment analysis in improving real-world stock forecasting systems.

College Research Work - Capstone Project

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Benchmarking TF-IDF, Word2Vec-LSTM, and Legal-BERT for Automated Legal Clause Classification

This study evaluates three NLP approaches—TF-IDF with SVM, Word2Vec-LSTM, and Legal-BERT—for classifying legal contract clauses. Through comparative analysis, it shows how transformer-based models capture domain-specific semantics more effectively than statistical and sequential embedding methods. Legal-BERT achieved ~93.7% accuracy and the highest precision and F1-scores, highlighting its superiority in handling complex legal language. The results demonstrate the value of contextual embeddings in building reliable and scalable LegalTech systems.

College Research Work – Natural Language Processing

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Certifications

AWS Academy Graduate - Cloud Architecting - Training Badge

Completed AWS Academy Cloud Architecting training, gaining expertise in designing and deploying scalable, secure, and cost-effective cloud solutions on AWS. Skilled in applying AWS best practices using the Well-Architected Framework.

AWS Academy

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Mastering Centralized Application Configuration in Azure: A Strategic Approach.

The workshop focused on key strategies for managing application configurations within the Microsoft Azure cloud environment. Key topics covered included best practices for secure configuration storage, with a specific focus on utilizing Azure App Configuration and Azure Key Vault.

NPTEL

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