WUST Research Center

Ongoing Research Projects

Machine Learning & AI

Project Title:

Advanced Machine Learning Frameworks for Prediction, Optimization, and Decision Intelligence

Overview:

This research project focuses on developing advanced machine learning and deep learning models for predictive analytics, optimization, and intelligent decision-making across multiple domains, including finance, healthcare, climate science, and industrial systems. The project integrates neural networks, ensemble learning, transformers, and graph-based models to handle large-scale, complex datasets.

Key Research Focus Areas:

Predictive modeling using CNNs, LSTMs, Transformers, and GNNs
Time-series forecasting (finance, sales, climate, emissions)
Optimization using hybrid ML and evolutionary algorithms
Explainable and trustworthy AI models

Current Outcomes:

High-accuracy financial forecasting models
Climate and rainfall prediction systems
Industrial fault prediction and optimization frameworks

Funding & Collaboration Relevance:

Aligned with NSF AI Institutes, DOE, NOAA, and industry analytics partnerships. Supports data-driven policy, economic forecasting, and smart systems.

Published papers:

Paper Name	Keywords
Gen-Optimizer: A Generative AI Framework for Strategic Business Cost Optimization	generative AI, business cost optimization, transformer model, LLM, transfer learning, NLP, cost efficiency, sustainability
Explainable Machine Learning for Understanding Trip Mode Choice: Evidence from the 2022 U.S. National Household Travel Survey	explainable AI, XAI, SHAP, trip mode choice, travel behavior, machine learning, transportation survey
DistilRoBiLSTMFuse: An Efficient Hybrid Deep Learning Approach for Sentiment Analysis	sentiment analysis, deep learning, NLP, DistilRoBERTa, BiLSTM, hybrid model, text classification
More papers

Cybersecurity & Privacy

Project Title:

AI-Driven Cybersecurity, Threat Intelligence, and Digital Infrastructure Protection

Overview:

This project develops intelligent cybersecurity systems using machine learning to detect, prevent, and mitigate cyber threats across aviation systems, IoT devices, healthcare networks, and enterprise platforms.

Key Research Focus Areas:

Phishing and social engineering detection using ML
Aviation cybersecurity (ADS-B spoofing and injection detection)
IoT security using IEEE-compliant AI frameworks
Privacy-aware and explainable cyber-defense systems

Current Outcomes:

Real-time ADS-B attack detection models
High-accuracy phishing detection systems
AI frameworks for securing IoT and medical devices

Funding & Collaboration Relevance:

Strong alignment with DoD, DHS, FAA, DARPA, and critical infrastructure security programs.

Published papers:

Paper Name	Keywords
Artificial Intelligence for Cybersecurity: A State of the Art	cybersecurity, AI, machine learning, threat detection, adversarial attacks, XAI, quantum computing
Optimizing Intrusion Detection with Hybrid Deep Learning Models and Data Balancing Techniques	intrusion detection, deep learning, SMOTE, data balancing, IDS, network security, hybrid model
Dynamic Secret Sharing for Enhanced Cloud Security: Tackling Eavesdropping and Threshold Attacks	secret sharing, cloud security, eavesdropping, threshold attacks, cryptography, distributed systems
Automating Malware Detection and Response via Real-Time Threat Feed Integration with Wazuh SIEM	malware detection, SIEM, Wazuh, threat intelligence, real-time response, cybersecurity automation
AI-Driven Secure Semantic Communication with Dynamic Encryption for Real-Time Data Integrity	semantic communication, dynamic encryption, AI security, data integrity, real-time, secure communication
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Healthcare & Medical Imaging

Project Title:

Explainable AI for Medical Imaging, Diagnostics, and Predictive Healthcare

Overview:

This project advances AI-powered healthcare by developing interpretable, high-accuracy diagnostic systems for early disease detection, medical imaging analysis, and healthcare system optimization.

Key Research Focus Areas:

Cancer detection (breast, brain, gallbladder) using deep learning
Ophthalmic biomarker detection with explainable AI
Alzheimer’s disease and neurological disorder analysis
IoT-enabled predictive healthcare and fraud detection

Current Outcomes:

Graph Neural Network models for cancer diagnosis
Explainable deep learning models for clinical decision support
AI-based healthcare fraud and risk detection systems

Funding & Collaboration Relevance:

Highly aligned with NIH, NCI, NIBIB, healthcare systems, and medical technology companies.

Published papers:

Paper Name	Keywords
A Hybrid Transfer Learning Framework for Brain Tumor Diagnosis	brain tumor, transfer learning, MRI, deep learning, VGG16, ResNet, XAI, medical imaging
OBoctNet: Enhancing Ophthalmic Biomarker Detection Through Active Learning and Explainable AI in Radiological Analysis	OCT, ophthalmic biomarker, active learning, explainable AI, GradCAM, semi-supervised, ophthalmology
DeepDenseVit: A Hybrid Deep Learning Approach for Ovarian Cancer Classification	ovarian cancer, deep learning, ViT, DenseNet, hybrid model, medical imaging, classification
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IoT, Robotics & Engineering

Project Title:

Intelligent IoT, Robotics, and Autonomous Engineering Systems

Overview:

This project focuses on intelligent cyber-physical systems integrating AI, IoT, robotics, and autonomous control for real-world engineering applications.

Key Research Focus Areas:

Autonomous drones and UAV route optimization
Self-driving agricultural and industrial machinery
Smart IoT sensor networks
AI-enhanced robotics for construction and infrastructure

Current Outcomes:

UAV navigation and optimization frameworks
Autonomous agricultural machinery studies
Smart infrastructure monitoring systems

Funding & Collaboration Relevance:

Aligned with NSF Engineering, USDA, DoD autonomy programs, and smart infrastructure initiatives.

Published papers:

Paper Name	Keywords
Digital Twin for Intelligent Context-Aware IoT Healthcare Systems	digital twin, IoT, healthcare, context-aware, intelligent systems, smart health, edge computing
An IoT-based Smart Home Automation System: Enhancing Security, Energy Efficiency, and Remote Accessibility	smart home, IoT, home automation, energy efficiency, security, remote access, embedded systems
LineCourier: A Semi-Autonomous Mobile Robot for Indoor Logistics	mobile robot, indoor logistics, semi-autonomous, navigation, line following, delivery robot
Smart IoT Egg Incubator System with Machine Learning for Damaged Egg Detection	IoT, egg incubator, machine learning, image processing, smart agriculture, embedded systems
More papers

The Transformative Role of Generative AI in Education

Project Title:

Generative AI for Optimization, Decision Support, and Intelligent Systems

Overview:

This project explores the transformative impact of Generative AI in business optimization, system design, and decision-making processes.

Key Research Focus Areas:

Generative models for cost optimization and planning
AI-assisted strategic decision-making
Synthetic data generation for model robustness
Ethical and responsible generative AI

Current Outcomes:

Generative AI frameworks for business cost optimization
AI-driven strategic modeling tools

Funding & Collaboration Relevance:

Aligned with NSF, industry R&D, enterprise AI initiatives, and responsible AI programs.

Published papers:

Paper Name	Keywords
EduBot: A Low-Cost Multilingual AI Educational Robot for Inclusive and Scalable Learning	educational robot, multilingual AI, NLP, inclusive education, low-cost, scalable learning, chatbot
Exploring Religions and Cross-Cultural Sensitivities in Conversational AI	conversational AI, religion, cross-cultural, NLP, bias, cultural sensitivity, LLM
CAMICS: A Context-Aware Multi-Intent Conversational System for Enhanced AI-Driven Customer Interaction Models	conversational AI, multi-intent detection, context-aware, NLP, customer interaction, dialogue systems
More papers

Agriculture & Environment

Project Title:

AI for Sustainable Agriculture, Climate Intelligence, and Environmental Monitoring

Overview:

This project develops AI-driven solutions for sustainable farming, food security, climate resilience, and environmental protection.

Key Research Focus Areas:

Crop disease and weed detection using deep learning
IoT-enabled smart farming and crop suitability analysis
Climate modeling and rainfall prediction
Emission forecasting and environmental analytics

Current Outcomes:

Multi-crop disease detection systems
Adaptive weed classification models
High-accuracy rainfall and emission prediction tools

Funding & Collaboration Relevance:

Strong alignment with USDA, EPA, NOAA, DOE, and sustainability-focused funding programs.

Published papers:

Paper Name	Keywords
A Multi-Scale Feature Extraction and Fusion Deep Learning Method for Classification of Wheat Diseases	wheat disease, deep learning, multi-scale feature extraction, CNN, plant pathology, image classification
ADeepWeeD: An Adaptive Deep Learning Framework for Weed Species Classification	weed classification, deep learning, adaptive framework, CNN, precision agriculture, plant identification
Internet of Things and AI Powered Crop Suitability Detection System for Sustainable Farming	IoT, crop suitability, AI, sustainable farming, precision agriculture, sensors, smart farming
Crop Recommendation: A Technology-Driven Approach to Enhancing Agricultural Productivity	crop recommendation, machine learning, agricultural productivity, smart farming, soil analysis, AI
More papers

Smart Mobility, Transportation & Energy Systems

Project Title:

AI for Smart Mobility, Transportation Safety, and Energy-Efficient Systems

Overview:

This project focuses on applying AI to transportation safety, electric and new-energy vehicles, and intelligent mobility systems.

Key Research Focus Areas:

AI-driven fault detection in New Energy Vehicles (NEVs)
Transportation system safety and optimization
Predictive maintenance for smart mobility systems
Renewable energy integration and efficiency modeling

Current Outcomes:

NEV fault prediction models
Transportation safety analytics
Energy optimization frameworks

Funding & Collaboration Relevance:

Aligned with DOE, DOT, FAA, automotive industry, and smart transportation initiatives.

Published papers:

Paper Name	Keywords
Explainable Machine Learning for Understanding Trip Mode Choice: Evidence from the 2022 U.S. National Household Travel Survey	trip mode choice, XAI, SHAP, travel behavior, NHTS, transportation, machine learning
Energy-Optimal Routing Optimization for Connected and Autonomous Vehicles in Urban Networks	autonomous vehicles, energy optimization, routing, urban networks, CAV, numerical modeling
Secure Energy Transactions Using Blockchain - Leveraging AI for Fraud Detection and Energy Market Stability	blockchain, energy transactions, fraud detection, AI, smart grid, energy market, security
Intelligent Streetlight Control System Using Machine Learning for Enhanced Energy Optimization in Smart Cities	smart city, streetlight control, machine learning, energy optimization, IoT, LED, urban infrastructure
More papers

Other Emerging Domains

Project Title:

Emerging AI Applications: Geospatial Intelligence, Smart Cities, and Digital Transformation

Overview:

This project explores emerging AI applications that support urban planning, renewable energy, smart cities, and digital transformation.

Key Research Focus Areas:

Geospatial AI and land-use analytics
Renewable energy planning (rooftop solar detection)
Smart city decision-support systems
AI-enabled digital governance

Current Outcomes:

YOLO-based rooftop solar mapping
Real-time land intelligence platforms
Urban analytics and planning tools

Funding & Collaboration Relevance:

Aligned with NSF Smart & Connected Communities, DOE renewable energy programs, and city/state innovation grants.

Published papers:

Paper Name	Keywords
Integrating AI and Geospatial Technologies for Sustainable Smart City Development: A Case Study of Yerevan	geospatial AI, land-use classification, CNN, SHAP, smart city, sustainable urbanization, urban growth, remote sensing, Sentinel-2
Explainable Spatially Explicit Geospatial Artificial Intelligence in Urban Analytics	GeoAI, graph neural networks, urban analytics, explainable AI, spatial data, GNN, XAI, city science
Deep Learning for Urban Land Use Category Classification: A Review and Experimental Assessment	urban land use, deep learning, CNN, transformer, remote sensing, social sensing, multimodal fusion, classification
SDI-Enabled Smart Governance: A Review (2015–2025) of IoT, AI and Geospatial Technologies — Applications and Challenges	spatial data infrastructure, SDI, smart governance, IoT, geospatial, AI analytics, cloud computing, urban decision-making
LandSin: A Differential ML and Google API-Enabled Web Server for Real-Time Land Insights and Beyond	land intelligence, real-time analytics, machine learning, Google API, web server, geospatial data, differential ML
Rooftops Detection with YOLOv8 from Aerial Imagery and a Brief Review on Rooftop Photovoltaic Potential Assessment	YOLOv8, rooftop detection, aerial imagery, photovoltaic, solar energy, object detection, YOLO, PV potential assessment
Deep Learning-Based Rooftop PV Detection and Techno-Economic Feasibility for Sustainable Urban Energy Planning	rooftop PV, deep learning, DeepLabv3+, solar potential, urban energy planning, satellite imagery, techno-economic analysis
Automated Rooftop Solar Panel Detection Through Convolutional Neural Networks	solar panel detection, CNN, aerial imagery, photovoltaic mapping, remote sensing, segmentation, renewable energy
Automatic Rooftop Solar Panel Recognition from UAV LiDAR Data Using Deep Learning and Geometric Feature Analysis	LiDAR, rooftop solar, UAV, deep learning, geometric features, MLP, solar mapping, renewable energy
Detecting Defects in Solar Panels Using the YOLO v10 and v11 Algorithms	solar panel defect detection, YOLOv10, YOLOv11, object detection, photovoltaic inspection, fault detection, aerial imagery
Artificial Intelligence for Smart Cities: A Comprehensive Review Across Six Pillars and Global Case Studies	AI, smart city, urban governance, mobility, energy, predictive analytics, e-governance, digital transformation, case studies
Leveraging AI to Enable Sustainable Urban Development Through Smart and Carbon-Free Cities	sustainable urban development, AI, carbon-free cities, ensemble learning, IoT, smart grid, predictive modeling, energy efficiency
Artificial Intelligence in Smart Cities — Applications, Barriers, and Future Directions: A Review	AI, smart city, smart governance, smart mobility, smart economy, barriers, literature review, urban AI
A Data-Driven Framework for Digital Transformation in Smart Cities: Integrating AI, Dashboards, and IoT Readiness	digital transformation, smart city, AI, IoT, dashboard, digital maturity index, NLP, public administration
Intelligent Streetlight Control System Using Machine Learning Algorithms for Enhanced Energy Optimization in Smart Cities	smart city, streetlight control, machine learning, energy optimization, IoT, LED, urban infrastructure, smart lighting
Generative AI Meets Future Cities: Towards an Era of Autonomous Urban Intelligence	generative AI, urban planning, land-use configuration, GAN, autonomous urban intelligence, geospatial AI, responsible AI
Geodesign in the Era of Artificial Intelligence	geodesign, AI, land use, spatial analysis, GAN, LSTM, transformer, heat island, green infrastructure, urban design
AI-Driven Data Governance for Smart Cities: Balancing Privacy, Efficiency, and Public Trust	AI governance, smart city, privacy, federated learning, differential privacy, algorithmic fairness, public trust, digital rights
Digital Technology and AI for Smart Sustainable Cities in the Global South: A Critical Review	smart sustainable city, digital technology, AI, Global South, IoT, big data, governance, digital divide, sustainability
How Will AI Transform Urban Observing, Sensing, Imaging, and Mapping?	AI, urban sensing, earth observation, satellite imagery, remote sensing, urban mapping, autonomous design, geospatial big data
More papers