Hera Shaheen
National Institute of Technology Patna, Central University of South Bihar
Research areas: Computer Science · Artificial Intelligence · Cutaneous Melanoma Detection and Management · AI in cancer detection · Advanced Neural Network Applications
About
Dr. Hera Shaheen is an Assistant Professor in the Department of Artificial Intelligence & Machine Learning at Patna Women's College, Patna, India. She earned her PhD in Computer Science & Engineering from the National Institute of Technology (NIT) Patna. Her research focuses on Artificial Intelligence, Machine Learning, Deep Learning, Computer Vision, Explainable AI, Vision Transformers, and Medical Image Analysis, with particular emphasis on AI-driven healthcare and skin cancer diagnosis. She has over 10 years of teaching experience and is actively involved in research, mentoring, and developing AI solutions for real-world applications.
Employment
Employment history is unavailable.
Education
- 2020 - 2025
-
Central University of South Bihar M. Tech(Computer Science)2017 - 2019
Projects & Funding
Projects & funding information is unavailable.
Publications (9)
- Assessment of Total Available Moisture (TAM) and ReadilyAvailable Moisture (RAM) for the Cultivation of Soybean inSaraikela Kharsawan District of Jharkhand Save
- Advanced skin lesion detection via efficientNetB0 and vision transformer model with spatial-aware attention Save
- Deep Learning-Based Automatic Skin Lesion Segmentation Save
- Multimodal Skin Cancer Classification: Optimized Convolutional Network with Customized Loss and RNN-Based FCNN Fusion Save
- Skin Cancer Classification using YOLO-NAS based Convolutional Network with Modified Focal Loss Save
- Multiclass skin cancer classification using particle swarm optimization and convolutional neural network with information security Save
- Skin lesion classification using HG-PSO and YOLOv7 based convolutional network in real time Save
- Ensemble Maximum Likelihood Estimation Based Logistic MinMaxScaler Binary PSO for Feature Selection Save
- Minmaxscaler binary pso for feature selection Save