Published
Foundational AI
Optimization of Deep Learning Algorithms for Large Digital Data Processing Using Evolutionary Neural Networks
AI that evolves its own network design to process massive datasets faster.
Research index
Every paper Dr. Nehzati has produced.
27 papers of deep research, spanning foundational methods, quantum-inspired models and frameworks, and deep learning at institutional scale, plus first-author hardware sensing work. 8 have been published in peer-reviewed journals. 14 are under peer review, and each one says so on its own card; 5 are technical reports on the production model line. Full text for every paper is hosted here.
8 published
14 under peer review
5 technical reports
Independently verifiable on Dr. Nehzati's Google Scholar profile.
Published
Published
Foundational AI
Integrating Convolutional Neural Networks for Improved Software Engineering
CNNs for software engineering under collaborative and unbalanced data conditions.
Published
Sensing hardware
Wearable Graphene-Patterned Gas Detection Sensor
A wearable gas sensor on patterned graphene. First author, with co-authors.
Published
Foundational AI
Robust Pedestrian Detection in Surveillance Videos via Fractal-Geometric Regularization of Conditional Random Fields
Spotting pedestrians in surveillance video reliably, even with occlusion and noise.
Published
Foundational AI
A Quantum-Inspired, Biomimetic, and Fractal Framework for Self-Healing AI Code Generation
Self-healing AI that writes and repairs its own code.
Published
Foundational AI
Self-Evolving AI: Metabolic Processing and Recursive Self-Representation
AI that keeps learning and rewires itself without retraining.
Published
Clinical machine learning
Federated Learning for 30-Day Readmission Prediction Across 47 Healthcare Institutions
Predicting 30-day readmissions across 47 institutions without moving patient data.
Published
Geometry of learning
Data Field Theory: A Geometric Framework for Learning on Riemannian Manifolds
The geometric framework underneath the lab's work on how learning behaves on curved data.
Under peer review
Submitted and being assessed by external reviewers. Listed here because the work exists and is readable, not because it has cleared review.
Under peer review
Foundational AI
A Multi-Scale Neuromorphic, Quantum-Inspired, and Biomimetic Framework for Fractal Data Integration
A brain-inspired architecture for integrating data at any scale.
Under peer review
Semantic intelligence
Autonomous Self-Healing Clinical Intelligence Networks with Biomimetic Energy-Information Processing
A self-healing clinical data network that harmonizes records and predicts outcomes.
Under peer review
Semantic intelligence
Semantic Intelligence Framework for Clinical Data Warehouses
Turning messy hospital records into clinically validated insights across 492,542 encounters.
Under peer review
Semantic intelligence
Semantic Intelligence Framework for Epic Clarity Data Warehouses
Turning Epic Clarity warehouse records into structured clinical concepts.
Under peer review
Foundational AI
Neuromorphic Edge Computing Framework for Real-Time Swarm Robotics
Neuromorphic edge AI for real-time coordination of robot swarms.
Under peer review
Computer vision
People Detection Optimization from a Partial Gait Cycle
Detecting and identifying people from a partial walking gait.
Under peer review
Semantic intelligence
Quantum-Superposition Clinical Intelligence Networks: Many-Worlds Data Harmonization
Many-worlds, quantum-inspired harmonization for population-scale health data.
Under peer review
Oncology
lncRNA HELC Drives Liver Cancer Proliferation via AP1-Dependent Regulation of Fatty Acid Synthase
A long non-coding RNA studied as a driver of liver-cancer growth.
Under peer review
Oncology
Reprogramming Drug Resistance Pathways Through Epigenetic Modulation in Advanced Lung Cancer
Reversing drug resistance in advanced lung cancer through epigenetic reprogramming.
Under peer review
Oncology
Integrating Deep Learning and Radiomics for Preoperative Glioma Grading: A Multi-Center MRI Study
Reading MRIs to grade brain tumors and predict molecular subtype before surgery.
Under peer review
Semantic intelligence
A Unified Mathematical Framework for Semantic Classification in Healthcare
The mathematics of how clinical data gets mapped and classified.
Under peer review
Semantic intelligence
A FHIR-Native Harmonization Overlay for Semantic Interoperability
A vendor-neutral overlay that makes any two FHIR systems understand each other.
Under peer review
Semantic intelligence
Harmonization-Aware Predictive Overlap Forecasting for Population-Scale Pharmaceutical Need and Utilization Sensing
Forecasting population-scale drug demand by separating true need from realized use.
Under peer review
Foundational AI
A Unified Ontology-Grounded Health Intelligence Framework for Aircraft Vibration Analytics and Predictive Maintenance
Turning aircraft vibration data into fleet-wide predictive maintenance intelligence.
No papers in that area.
Models and technical reports
Essay
Interoperability
Interoperability moves data. Meaning is the missing layer.
Why moving records is not the same as understanding them.
Technical report
Model architecture
Semantic Classification Engine (SCE): multi-ontology, customer-adaptive semantic understanding
The engine that classifies raw healthcare fields against shared clinical ontologies and adapts to each customer's data.
Technical report
Model architecture
Data Mapper Model (DMM): multi-standard, bi-directional mapping synthesis
Synthesizes mappings between healthcare data standards in both directions, rather than maintaining hand-built crosswalks.
Technical report
Model architecture
Data Harmonization Model (DHM): continuous multi-site harmonization, self-healing
Keeps multi-site healthcare data harmonized continuously, repairing drift as sources change.
Technical report
Model architecture
Emergent Mapping Intelligence (EMI): multi-model architecture for orchestration and scale
Orchestrates the lab's classification, mapping and harmonization models as one system at production scale.
Technical report
Model architecture
Self-Enriching Calibrated Neighborhood Operators on the Concept Hilbert Space
Operators on a concept Hilbert space that enrich themselves as they classify. The most formal statement of meaning as geometry.
In the newsroom
When a paper on this page clears peer review, the lab says so. These are the announcements behind the published work above.
- Self-healing AI code generation accepted for publication, the Frontiers in Artificial Intelligence paper listed above.
- DFT Labs publishes Data Field Theory in Frontiers in Big Data, the geometry paper listed above.
- HeyDonto publishes its core AI architecture.
- Everything else in the newsroom.