Mariofanna Milanova Data-verified
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Biography and Research Information
OverviewAI-generated summary
Mariofanna Milanova's research interests encompass the application of advanced computational techniques to diverse fields, including mental health, finance, and engineering.
Her work in mental health has focused on the assessment and treatment of negative symptoms in schizophrenia, investigating new approaches for diagnosis and intervention. In parallel, Milanova has explored the capabilities of deep learning and hybrid models for financial time series forecasting, aiming to improve prediction accuracy. Her research also extends to computer vision and machine learning applications, such as the development of intelligent service recommendation systems, eye-gaze writing optimization, and the identification and tracking of railway components. Additionally, she has investigated novel EEG classification methods for seizure epilepsy detection and explored performance analyses of deep learning model-compression techniques for audio classification on edge devices.
Milanova leads a research group at the University of Arkansas at Little Rock and has a substantial publication record, with 208 total publications and an h-index of 20. She has received federal funding from the NSF for her work on Computer Vision-Based Intelligent Service Recommendation Systems. Her collaborations include several researchers from the University of Arkansas at Little Rock, such as Afsana Mou, Md Imran Sarker, Md Rizwanul Kabir, and Ehsan Nasiri.
Metrics
- h-index: 17
- Publications: 177
- Citations: 1,090
Selected Publications
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Federated Learning-Based Road Defect Detection with Transformer Models for Real-Time Monitoring (2025)
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Parsing Requirements for Automatic Prompting of Large Language Models for Requirements Validation (2025)
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Entity Resolution Using Transformers for Synthetic Datasets (2025)
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Deep Neural Network with RIS-Powered Wireless Communication Systems for Channel Modeling (2025)
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Semantic Entity Resolution on Synthetic Datasets: A Transformer-Centric Approach (2025)
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Unveiling Alzheimer’s Progression: AI-Driven Models for Classifying Stages of Cognitive Impairment Through Medical Imaging (2025)
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Large Language Models for Classification of Functional and Nonfunctional Requirements (2025)
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Towards Smarter Road Maintenance: YOLOv7-Seg for Real-Time Detection of Surface Defects (2025)
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A Comparative Study of YOLO, SSD, Faster R-CNN, and More for Optimized Eye-Gaze Writing (2025)
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LSTM–Transformer-Based Robust Hybrid Deep Learning Model for Financial Time Series Forecasting (2025)
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Novel EEG feature selection based on hellinger distance for epileptic seizure detection (2025)
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Enhancing Bias Assessment for Complex Term Groups in Language Embedding Models: Quantitative Comparison of Methods (2024)
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Novel EEG Classification Based on Hellinger Distance for Seizure Epilepsy Detection (2024)
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Deep learning based identification and tracking of railway bogie parts (2024)
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Blockchain as a Service (2024)
Federal Grants 1 $50,000 total
I-Corps: Computer Vision-Based Intelligent Service Recommendation System
Collaboration Network
Top Collaborators
- Performance Analysis of Deep Learning Model-Compression Techniques for Audio Classification on Edge Devices
- Intelligent Traffic Control System Using YOLO Algorithm for Traffic Congested Cities
- Active Learning Monitoring in Classroom Using Deep Learning Frameworks
- Deep Learning Approaches for Classroom Audio Classification Using Mel Spectrograms
- Performance Analysis of Deep Learning Model Compression Techniques for Audio Classification on Edge Devices
- Deep Learning-Based Multimodal Image Retrieval Combining Image and Text
- Video-Based Monitoring and Analytics of Human Gait for Companion Robot
- Video Analytics Gait Trend Measurement for Fall Prevention and Health Monitoring
- Explaining Multimodal Image Retrieval Using A Vision and Language Task Model
- Explaining Multimodal Image Retrieval Using A Vision and Language Task Model
- Semantic Entity Resolution on Synthetic Datasets: A Transformer-Centric Approach
- Entity Resolution Using Transformers for Synthetic Datasets
- LSTM–Transformer-Based Robust Hybrid Deep Learning Model for Financial Time Series Forecasting
- Semantic Entity Resolution on Synthetic Datasets: A Transformer-Centric Approach
- Entity Resolution Using Transformers for Synthetic Datasets
- Video-Based Monitoring and Analytics of Human Gait for Companion Robot
- Video Analytics Gait Trend Measurement for Fall Prevention and Health Monitoring
- Video Surveillance Framework Based on Real-Time Face Mask Detection and Recognition
- Masked Face Detection Using Artificial Intelligent Techniques
- Video Surveillance Framework Based on Real-Time Face Mask Detection and Recognition
- Masked Face Detection Using Artificial Intelligent Techniques
- Active Learning Monitoring in Classroom Using Deep Learning Frameworks
- Deep Learning Approaches for Classroom Audio Classification Using Mel Spectrograms
- Case series: Cariprazine in early-onset schizophrenia
- Application of combined drug and music therapy in patients with depression
- Graph convolutional networks for pain detection via telehealth
- List of contributors
- Deep learning based identification and tracking of railway bogie parts
- Towards Smarter Road Maintenance: YOLOv7-Seg for Real-Time Detection of Surface Defects
- Deep learning based identification and tracking of railway bogie parts
- Towards Smarter Road Maintenance: YOLOv7-Seg for Real-Time Detection of Surface Defects
- A Comparative Study of YOLO, SSD, Faster R-CNN, and More for Optimized Eye-Gaze Writing
- Advancing Eye-Gaze Writing through the Integration of Computer Vision and Predictive Text
- Large Language Models for Classification of Functional and Nonfunctional Requirements
- Parsing Requirements for Automatic Prompting of Large Language Models for Requirements Validation
- Multi-Agent RAG Framework for Entity Resolution: Advancing Beyond Single-LLM Approaches with Specialized Agent Coordination
- Semantic Entity Resolution on Synthetic Datasets: A Transformer-Centric Approach
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