Impact

By the numbers.

110+

Publications

Journals, conferences, and books across AI, ML, and data science.

110+

Global collaborators

Partner institutions across 30+ countries.

2019

Igniting since

Applied intelligence, from idea to impact.

Lineage

Academic genealogy.

The scholarly lineage behind Applied INtelligence Lab.

Identity

Why Applied INtelligence Lab.

Rigor, curiosity, and impact — by design.

Applied intelligence first

Research built for real environments, with usable and measurable outcomes.

A playground for discovery

A place to explore ideas, test hypotheses, and iterate fast.

Collaboration at the core

Disciplines and institutions working together on responsible AI.

Purpose

Mission & vision.

Where curiosity meets purpose.

Our Mission

Advance the science and application of AI, machine learning, and data science to create intelligent, sustainable, and interconnected systems — from smart cities and IoT to green computing.

Our research bridges theory and real-world impact, combining academic excellence with practical innovation.

"Applied INtelligence Lab is not just a lab — it's a playground for ideas, collaboration, and discovery." (–Director, Applied INtelligence Lab)

Our Vision

A world where intelligent systems enhance human life responsibly and sustainably — a hub where curiosity meets purpose.

We are committed to:

Advancing AI and intelligent systems that are explainable and scalable.
Building collaborative bridges across academia, industry, and research communities.
Educating the next generation of AI innovators.

Core domains

Research areas.

From raw data to applied intelligence.

Data

Sensor & multimodal inputs
IoT sensors, images/3D scans, biosignals and multimodal fusion.
Time-series & sequence streams
Forecasting, fusion, sequence modelling pipelines.
Representation & features
Autoencoders, representation learning, transfer & pixel-level feature engineering.

Intelligence

Deep & sequence learners + transformers
CNNs, LSTM, hybrids, Tab/domain transformers and LLM methods.
Ensembles & optimization
Stacking/bagging/meta-ensembles, hyperparameter tuning and meta-heuristics.
Explainability & structured models
Explainable AI, graph-attention/graph methods, statistical & signal-fusion techniques.

Applications

Health & human systems
Medical diagnostics, physiological monitoring, pose/physiotherapy analysis.
Agri / environment / energy
Plant disease detection, crop & AQI forecasting, flood risk, battery SOH and energy forecasting.
Industry & security
Manufacturing, finance, cybersecurity / fraud detection.

Partnership

Collaborate with us.

Academia, industry, and future researchers — together.

Collaborate With Applied INtelligence Lab

We welcome research partners, visiting scholars, and industry collaborators who share our vision of advancing AI for a sustainable and connected world.

At AINTLab, collaboration is at the heart of everything we do. We work across disciplines—from ML and intelligent systems to sustainability and DS—to build AI solutions that matter.

We actively collaborate with:

Universities and research institutes on joint projects and publications.
Industry partners seeking AI expertise in data analytics, and intelligent systems.
Students and visiting researchers looking to learn, contribute, and grow in an innovative environment.

Collaborator Network Map

Explore the live 3D network →

Countries with 4+ co-authored works, live from our publication DOIs — or contact us to join the map.

Network

Our collaborators.

Joint publications, projects, and visiting programs worldwide.

Selected works

Featured research.

Highlights from Q1 journals — view all publications.

FLTrans-Net: Transformer-based feature learning network for wheat head detection

Research Summary

DataWheat-field images with overlapping small spikes, complex backgrounds; multi-scale visual features for detection.
IntelligenceFLTrans-Net: transformer-based multi-scale fusion, spatial attention, and lightweight RetinaNet for noise-robust feature learning.
ApplicationsReal-time wheat-head detection for yield assessment and field management on resource-constrained devices.

Published in Computers and Electronics in Agriculture (Elsevier, 2025) - SCIE (Q1); IF=8.9; Rank=2/94; IF(%)=1.6%.

AE-BPNN: autoencoder and backpropagation neural network-based model for lithium-ion battery state of health estimation

Research Summary

DataEIS measurements from Li-ion cells across multiple temperatures and operating states.
IntelligenceAE-BPNN with SCG/RBP optimization for feature reduction and State-of-Health (SOH) estimation.
ApplicationsAccurate battery SOH prediction for energy storage systems.

Published in Scientific Reports (Nature Portfolio, 2025) - SCIE (Q1); IF=3.9; Rank=25/136; IF(%)=18.0%.

Tweeting Circular Economy: Unveiling Current Discourse Through Natural Language Processing

Research Summary

Data389k Twitter posts on circular economy (CE) (2012-2022).
IntelligenceNLP-based theme extraction and trend analysis.
ApplicationsPublic insight for CE policy and stakeholder engagement.

Published in Sustainable Development (Wiley, 2025) - SSCI (Q1); IF=9.9; Rank=1/63; IF(%)=0.8%.

Newsroom

Latest updates.

Fresh from the lab — view all updates.

Get involved

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