Flagship Conference Track
Track 1 (Major Track): Artificial Intelligence in Higher Education
Track Chair: Prof. Sonali Agarwal, Department of Information Technology, IIIT Allahabad, India
Track Scope & CFP Objectives: Explores the fundamental disruption and enhancement of higher education through intelligent pedagogical agents, NEP 2020 curriculum transformation, automated research synthesis, institutional governance analytics, and ethical AI policies.
Sub-Theme 1.1
AI-Enabled Teaching & Learning Pedagogies
1.1.1
Intelligent Tutoring Systems (ITS): Adaptive learning algorithms, mastery-based student progression, and personalized cognitive modeling.
1.1.2
Generative AI in Assessment: Automated rubric generation, formative feedback bots, and open-ended essay evaluation frameworks.
1.1.3
Learning Analytics & Telemetry: Early-warning student retention scoring, behavioral engagement telemetry, and dropout risk modeling.
1.1.4
Immersive Virtual Laboratories: XR/VR simulations, digital twins for laboratory experiments, and interactive 3D STEM pedagogies.
1.1.5
Conversational AI Mentors: 24/7 autonomous academic advising, multimodal chat agents, and career guidance bots.
Sub-Theme 1.2
Curriculum Transformation, NEP 2020 & Future Skills
1.2.1
NEP 2020 & NCrF Alignment: Outcome-based AI curriculum, interdisciplinary STEM/non-STEM integration, and credit bank systems.
1.2.2
Micro-Credentials & Stackable Certifications: Industry 4.0/5.0 skill mapping, automated competency verification, and MOOC pathways.
1.2.3
Cross-Discipline AI Literacy: Computational thinking pedagogies across Humanities, Social Sciences, Law, and Design.
1.2.4
Prompt Engineering & Co-Intelligence: Human-AI collaborative workflows, critical output verification, and faculty empowerment.
Sub-Theme 1.3
AI in Academic Research & Scholarly Innovation
1.3.1
Automated Literature Discovery: Graph neural networks for systematic reviews, citation discovery, and scientometrics.
1.3.2
Scientific Machine Learning: Automated hypothesis generation, synthetic research datasets, and interdisciplinary discovery.
1.3.3
Scholarly Publishing Integrity: AI-assisted peer-review screening, citation manipulation detection, and predatory journal filters.
1.3.4
Grant & Patent Intelligence: Research funding opportunity matching, patent landscape intelligence, and tech transfer.
Sub-Theme 1.4
Institutional Governance & Campus Intelligence
1.4.1
Smart Campus Architectures: IoT energy telemetry, classroom occupancy optimization, and automated resource scheduling.
1.4.2
Predictive Admissions & Enrolment: Demographic yield modeling, student lifecycle analytics, and scholarship allocation AI.
1.4.3
Accreditation Dashboards: NAAC, NIRF, NBA, and QS ranking compliance management and automated evidence curation.
1.4.4
Secure Examination AI: Continuous digital proctoring, multi-modal biometric authentication, and fraud prevention frameworks.
Sub-Theme 1.5
Ethics, Equity, Policy & Multilingual Inclusion
1.5.1
Academic Integrity in GenAI: Plagiarism vs. co-creation policies, watermark verification, and authorship disclosure standards.
1.5.2
Algorithmic Fairness: Auditing fairness in automated grading, demographic equity, and mitigating bias in learning analytics.
1.5.3
Student Data Privacy (DPDP 2023 & GDPR): Ethical telemetry, consent architectures, and privacy-preserving learner analytics.
1.5.4
Vernacular & Multilingual AI: Indian language LLMs, Bhashini integration, and speech-to-text accessibility for rural learners.
Track 2 (Special Track): Artificial Intelligence in Agriculture & Smart Farming
Track Chair: Dr. Priti Srinivas Sajja, Professor & Director, PG Dept. of Computer Science, Sardar Patel University, Gujarat
Track Scope & CFP Objectives: Welcomes novel research submissions on computer vision, sensor informatics, autonomous field robotics, and data analytics addressing sustainable food production, precision farm protection, soil telemetry, and resilient agro-supply chains.
Sub-Theme 2.1
Precision Agriculture, Remote Sensing & Drone Telemetry
2.1.1
UAV & Satellite Crop Telemetry: Multispectral/hyperspectral aerial imaging, vegetation index (NDVI) analytics, and canopy density mapping.
2.1.2
High-Throughput Field Phenotyping: Computer vision plant trait measurement, growth stage classification, and biomass estimation.
2.1.3
GIS & Spatial Field Analytics: Elevation-guided soil fertility mapping, micro-climate zoning, and terrain-aware crop planning.
Sub-Theme 2.2
Crop Health, Disease Diagnosis & Yield Prediction
2.2.1
Vision-Based Plant Pathology: Deep learning foliar disease classification, fungal lesion segmentation, and early blight identification.
2.2.2
Pest Outbreak Prediction & Weeds: Autonomous pest swarm modeling, selective herbicide spraying vision models, and bio-defense AI.
2.2.3
Harvest Yield Forecasting Models: Multi-modal weather, soil, and satellite data fusion for pre-harvest harvest volume prediction.
Sub-Theme 2.3
Smart Irrigation, Soil Informatics & Climate Resilience
2.3.1
IoT Precision Irrigation: Closed-loop soil moisture optimization, evapotranspiration forecasting, and automated micro-drip systems.
2.3.2
Soil Chemistry & Nutrient Diagnostics: Optical spectroscopy for NPK nutrient sensing, organic matter estimation, and soil microbiome telemetry.
2.3.3
Climate-Resilient Agriculture: Drought and flood vulnerability scoring, extreme weather forecasting, and regenerative carbon modeling.
Sub-Theme 2.4
Agri-Robotics, Autonomous Machinery & Post-Harvest
2.4.1
Autonomous Field Robots & Tractors: GNSS/LiDAR autonomous field navigation, robotic seeding, weeding, and selective fruit harvesting.
2.4.2
Post-Harvest Sorting & Grading AI: Computer vision produce grading, defect detection, automated sorting, and shelf-life prediction.
2.4.3
Precision Livestock & Dairy Informatics: Cattle biometric health tracking, thermal imaging mastitis detection, and automated milking.
Sub-Theme 2.5
Agri-Food Supply Chain, Traceability & Market Intelligence
2.5.1
Farm-to-Fork Blockchain Provenance: Immutable supply chain tracking, pesticide residue compliance, and organic food authentication.
2.5.2
Commodity Price Trend Analytics: Reinforcement learning for agricultural market price prediction, demand forecasting, and inventory hedging.
2.5.3
Cold-Chain Telemetry & Loss Prevention: Real-time perishable sensor logging, spoilage alerts, and dynamic routing optimization.
Track 3 (Special Track): AI in Healthcare & Pharmaceutical Sciences
Track Chair: Prof. Amlan Chakrabarti, Professor and Director, A.K. Choudhury School of IT, University of Calcutta, Visiting Prof. Dept. of AI IIT Kharagpur & Adj. Prof. IIIT Delhi
Track Scope & CFP Objectives: Calls for original research in computer-aided diagnostics, generative drug formulation, multi-omics biomarker discovery, wearable IoMT telemetry, and privacy-preserving federated medical intelligence.
Sub-Theme 3.1
Medical Imaging, Computer-Aided Diagnostics & Oncology AI
3.1.1
Deep Learning Radiology & Pathology: Automated CT, MRI, Ultrasound, and X-ray segmentation for tumor, fracture, and lesion detection.
3.1.2
Early-Stage Oncology Screening: Digital histopathology, cell morphology classification, and multi-modal cancer staging models.
3.1.3
Cardiovascular & Neuroimaging AI: Echocardiogram interpretation, stroke risk stratification, and Alzheimer's/Parkinson's early biomarker prediction.
Sub-Theme 3.2
Computational Drug Discovery & Molecular Pharmacology
3.2.1
De Novo Molecular Design & Generation: Generative diffusion and transformer models for novel drug candidates and chemical space exploration.
3.2.2
Molecular Docking & Target Screening: 3D protein structure prediction (AlphaFold integrations), binding affinity scoring, and virtual screening.
3.2.3
ADMET & Toxicity Prediction: Machine learning models for Absorption, Distribution, Metabolism, Excretion, and Toxicity profiling.
Sub-Theme 3.3
Genomics, Multi-Omics & Precision Medicine
3.3.1
Multi-Omics Data Integration: Merging transcriptomics, proteomics, and epigenomics for individualized disease etiology.
3.3.2
Predictive Biomarker Discovery: Machine learning for patient-specific drug response prediction, immunotherapy targeting, and pharmacogenomics.
3.3.3
CRISPR & Gene Editing Optimization: Off-target effect prediction and guide-RNA sequence optimization using deep neural networks.
Sub-Theme 3.4
IoMT, Wearable Biosensors & Remote Patient Telemetry
3.4.1
Continuous Vitals Telemetry: PPG, ECG, and continuous glucose monitoring with real-time arrhythmia and hypoxia anomaly alerts.
3.4.2
Smart ICU & Telemedicine Systems: Sepsis onset prediction, acute respiratory distress scoring, and automated hospital triage assistants.
3.4.3
Surgical Robotics & Vision: AR-assisted practical guidance, tool tracking, and real-time anatomical boundary detection.
Sub-Theme 3.5
Federated Healthcare Learning, Privacy & Clinical Ethics (XAI)
3.5.1
Federated Learning for Clinical Data: Cross-institutional collaborative model training without patient data transfer (HIPAA/GDPR compliant).
3.5.2
Explainable AI (XAI) in Clinical Decisions: Interpretable decision support, feature attribution heatmaps, and clinician trust calibration.
3.5.3
Biomedical Synthetic Data: Privacy-preserving generative models (GANs/VAEs) for training on rare clinical conditions.
Track 4 (Special Track): AI in Emerging & Futuristic Technologies
Track Chair: Dr. Sridaran Rajagopal, Executive Dean, Faculty of Computer Applications & Academic Quality Assurance, Ganpat University
Track Scope & CFP Objectives: Unveils cutting-edge frontiers in quantum neural architectures, low-power neuromorphic edge hardware, swarm robotics, cybersecurity zero-trust AI, and sustainable energy-aware foundation models.
Sub-Theme 4.1
Quantum AI, Quantum Machine Learning & Hybrid Computing
4.1.1
Quantum Machine Learning (QML): Variational quantum eigensolvers, quantum neural networks (QNN), and quantum kernel methods.
4.1.2
Quantum-Safe Cryptography: Post-quantum cryptographic protocols, lattice-based cryptography, and quantum key distribution.
4.1.3
Hybrid Classical-Quantum Computing: Quantum-accelerated optimization for logistics, material science, and high-energy physics.
Sub-Theme 4.2
Edge AI, TinyML & Neuromorphic Embedded Systems
4.2.1
TinyML on Ultra-Low-Power MCUs: Model quantization, weight pruning, and real-time inference on milliwatt IoT devices.
4.2.2
Neuromorphic & Spiking Networks (SNN): Event-based vision sensor processing and brain-inspired asynchronous computing.
4.2.3
Edge Autonomous Robotics: Real-time LiDAR SLAM, sensor fusion, and multi-robot obstacle avoidance on embedded GPUs.
Sub-Theme 4.3
Autonomous Systems, Swarm Robotics & Spatial Twins
4.3.1
Multi-Agent Swarm Intelligence: Decentralized drone swarms, cooperative task allocation, and autonomous search-and-rescue algorithms.
4.3.2
Cyber-Physical Digital Twins: Industrial metaverse simulations, real-time factory floor telemetry, and predictive machinery maintenance.
4.3.3
Brain-Computer Interfaces (BCI): Neural signal decoding, EEG-based assistive device control, and cognitive state classification.
Sub-Theme 4.4
Cybersecurity AI, Deepfake Forensics & Zero-Trust Defense
4.4.1
Autonomous Threat Intelligence: Zero-day malware classification, behavioral anomaly detection, and automated SIEM/SOAR incident response.
4.4.2
Deepfake & Synthetic Media Forensics: Multi-modal biometric verification, GAN artifact detection, and audio-visual authentication.
4.4.3
Adversarial ML Robustness: Defense against evasion attacks, data poisoning mitigation, and certified model robustness.
Sub-Theme 4.5
Green AI, Sustainable Computing & Decentralized Web3 AI
4.5.1
Energy-Efficient Model Training: Carbon-aware compute scheduling, sparse foundation models, and green datacenter optimization.
4.5.2
Blockchain-Governed AI & Smart Contracts: Decentralized model ownership, verifiable computation, and decentralized compute marketplaces.
4.5.3
AI for Renewable Energy Grids: Power grid load forecasting, solar/wind fluctuation smoothing, and microgrid energy routing.
Track 5 (Special Track): Artificial Intelligence in Business
Track Chair: Prof. (Dr.) Sanjay Fuloria, Professor & Director, CDOE, IFHE Hyderabad
Track Scope & CFP Objectives: Investigates how artificial intelligence transforms modern commerce, corporate strategy, autonomous organizational agents, predictive marketing analytics, human resource management, FinTech risk modeling, tech entrepreneurship, responsible AI governance, cybersecurity, and ESG sustainability.
Sub-Theme 5.1
AI Strategy, Intelligent Organizations & Future of Work
5.1.1
AI, Strategy & Business Transformation: Strategic AI roadmaps, digital capability maturity models, enterprise competitive advantage, and cross-functional digital transformation.
5.1.2
Generative AI, AI Agents & Intelligent Organizations: Multi-agent autonomous enterprise systems, LLM workflow orchestration, intelligent robotic process automation (RPA), and enterprise knowledge graphs.
5.1.3
AI in HR & Future of Work: Talent acquisition analytics, employee retention scoring, autonomous skill-gap mapping, workforce reskilling pedagogies, and human-AI collaborative workflows.
Sub-Theme 5.2
Marketing Intelligence, Consumer Insights & Entrepreneurship
5.2.1
AI in Marketing & Consumer Insights: Predictive customer journey modeling, real-time hyper-personalization, consumer sentiment NLP, customer lifetime value (CLV) optimization, and algorithmic marketing orchestration.
5.2.2
AI, Entrepreneurship & Business Model Innovation: AI-native startup venture dynamics, dynamic platform pricing algorithms, disruptive business model prototyping, and venture capital AI valuation frameworks.
Sub-Theme 5.3
FinTech, Governance, Cybersecurity & ESG Growth
5.3.1
AI in Finance, FinTech & Risk: Automated credit scoring, financial fraud and AML anomaly detection, algorithmic quantitative trading, regulatory compliance automation (RegTech), and stress-test risk analytics.
5.3.2
Responsible AI, Ethics, Governance & Cybersecurity: Corporate AI policy governance, algorithmic bias audits, zero-trust AI security architecture, enterprise model explainability, and regulatory compliance (EU AI Act, DPDP).
5.3.3
AI, Sustainability, ESG & Inclusive Growth: Corporate carbon accounting telemetry, green supply chain optimization, automated ESG disclosures, and AI-driven inclusive economic empowerment.