LAB-TA

Department of Computer Engineering · Faculty Research Lab

Prof. Tanvir Ahmad

Professor & Head of Department

Investigating scalable graph mining algorithms, domain-adapted NLP pipelines, and network-layer security architectures for high-volume, heterogeneous datasets in enterprise and academic environments.

Graph AlgorithmsNLP PipelinesBig Data ETLNetwork Security

Collaborate with this Lab →Lab Publications (5) ↓

Research Specialisations

Areas of active investigation.

01Text Mining & Graph Mining

02Big Data Analytics

03Natural Language Processing (NLP)

04Information Security & Network Systems

Principal Investigator · Profile

Prof. Tanvir Ahmad

Professor & Head of Department

Department of Computer Engineering

HOD and Professor specialising in large-scale NLP, graph-structured data mining, and information security systems.

Propose Collaboration →Apply for Lab Fellowship

Laboratory Literature

Published papers, preprints & inventions.

[LAB-TA]Text Mining & Natural Language Processing2019-06-12[PEER-REVIEWED]Citations: 48+

Hybrid Filter–Wrapper Feature Selection Method for Sentiment Classification

Ansari, G., Ahmad, T., Doja, M. N.

Arabian Journal for Science and Engineering (Springer)

This paper proposes a hybrid filter-wrapper feature selection framework for high-dimensional sentiment classification tasks. The method achieves significant dimensionality reduction while preserving contextual polarity semantics across unstructured online customer review corpora.

#Sentiment Analysis#NLP#Feature Selection#Text Mining#Information Retrieval

DOI: 10.1007/s13369-019-03912-8 ↗View in Main Bibliography →

[LAB-TA]Information Security & Network Systems2020-04-18[PEER-REVIEWED]Citations: 112+

Land Records on Blockchain for Implementation of Land Titling in India

Thakur, V., Doja, M. N., Dwivedi, Y. K., Ahmad, T., Khanna, G.

International Journal of Information Management (Elsevier)

This study investigates the integration of distributed ledger technology (DLT) into public administration systems for land titling in India. By deploying blockchain-based cadastral record management, the work demonstrates immutable, cryptographically verifiable state management for state-scale governmental land registries.

#Blockchain#Data Integrity#Distributed Ledgers#Public Records#Security

DOI: 10.1016/j.ijinfomgt.2019.102097 ↗View in Main Bibliography →

[LAB-TA]Computer Vision & Deep Learning2020-07-15[PEER-REVIEWED]Citations: 61+

Object Detection Through Modified YOLO Neural Network

Ahmad, T., Ma, J., Yahya, M.

Scientific Programming (Hindawi)

This paper presents modifications to the YOLO (You Only Look Once) neural network architecture to enhance real-time object detection performance. The proposed optimizations yield improved accuracy and inference speed for computer vision applications in resource-constrained deployment environments.

#YOLO#Object Detection#Deep Learning#Computer Vision#Real-Time Inference

DOI: 10.1155/2020/8403262 ↗View in Main Bibliography →

[LAB-TA]Big Data Analytics & Biomedical Informatics2022-02-10[PEER-REVIEWED]Citations: 34+

Data Mining and Machine Learning in Cancer Survival Research: An Overview and Future Recommendations

Kaur, I., Doja, M. N., Ahmad, T.

Journal of Biomedical Informatics (Elsevier)

A comprehensive survey of data mining and machine learning techniques applied to cancer survival research. The paper evaluates predictive models and time-range-based sequential mining approaches across heterogeneous genomic and clinical oncology datasets, recommending future research directions for the field.

#Big Data Analytics#Healthcare Informatics#Machine Learning#Oncology#Survival Analysis

DOI: 10.1016/j.jbi.2022.103986 ↗View in Main Bibliography →

[LAB-TA]Big Data Analytics & AI-Driven Drug Discovery2026-01-10[PEER-REVIEWED]Citations: 7+

Advancements in Molecular Property Prediction: A Survey of Single and Multimodal Approaches

Ahmad, T., Doja, M. N., Kaur, I., Beg, M. M. S.

Archives of Computational Methods in Engineering (Springer)

This survey systematically reviews advancements in molecular property prediction using single-modal and multimodal deep learning models. The work evaluates graph neural networks, transformers, and hybrid multimodal architectures for AI-driven drug discovery, highlighting benchmark comparisons and open challenges in computational chemistry.

#Molecular Property Prediction#Drug Discovery#Multimodal Deep Learning#Graph Neural Networks#AI for Science

View in Main Bibliography →

Laboratory Mission Statement

Text Mining, Graph Mining & Big Data Intelligence Lab

Investigating scalable graph mining algorithms, domain-adapted NLP pipelines, and network-layer security architectures for high-volume, heterogeneous datasets in enterprise and academic environments.

Apply for Fellowship →All Publications

Prof. Tanvir Ahmad

Professor & Head of Department

Investigating scalable graph mining algorithms, domain-adapted NLP pipelines, and network-layer security architectures for high-volume, heterogeneous datasets in enterprise and academic environments.

Graph AlgorithmsNLP PipelinesBig Data ETLNetwork Security