Dr.Yasser Ismail

Immersive Technologies for Computational Thinking and Cybersecurity in Additive Manufacturing:      I actively mentor students in research projects involving Virtual Reality (VR), the Cave Automatic Virtual Environment (CAVE), and Cybersecurity-Additive Manufacturing (CAM) applications. Under my guidance, students explore immersive technologies to develop computational thinking (CT) skills and address cybersecurity challenges in advanced manufacturing contexts. I lead and support student-driven investigations that integrate VR tools into STEM education and workforce training, and I collaborate on CAVE-based teaching and research activities. These mentorship efforts have led to peer-reviewed conference publications and fostered student interest in interdisciplinary, application-focused research at the intersection of education, security, and manufacturing.

Automated Cancer Cell Classification and Detection Using Machine Learning and Image Processing: Developing a computerized system for classifying and detecting cancer cells using machine learning and image processing techniques. We aim to evaluate machine learning model performance by testing them on original, augmented, and processed medical images. The study leverages advanced image processing methods to improve image quality and applies machine learning models, such as VGG16, to enhance cancer detection accuracy, addressing the challenges of manual diagnosis and supporting future research in the field.

FPGA Hardware Accelerator for Real-Time Applications: Focused on Driver Drowsiness Detection Using YOLOv8 and Vivado High-Level Synthesis (HLS) Tools.

Integration of Renewable Energy Systems with AI-Driven Energy Management: Focused on optimizing the performance, efficiency, and reliability of hybrid energy systems by combining organic solar cells (OSCs) and supercapacitors. This research emphasizes the development of AI-driven energy management algorithms to enhance energy storage, distribution, and sustainability for applications in smart grids, IoT devices, and energy-efficient buildings.

Development of Advanced Materials for Sustainable Energy Solutions: Dedicated to exploring innovative materials, including graphene-based nanostructures and organic semiconductors, to improve energy generation and storage technologies. This research seeks to address critical challenges in energy density, environmental resilience, and scalability to advance green hydrogen production, solar energy conversion, and next-generation supercapacitors.

Machine-Learning Models for Corrosion Classification Levels: Developed machine learning-based algorithms to inspect and determine undesirable alloy phases distributed along grain boundaries for the Al-5XXX series. These machine-learning algorithms can detect different levels of corrosion for such alloy series.

Agriculture–Based Image Processing and Machine Learning: Develop a system that can monitor and determine the effect of adding chemical materials to plants and soils. This work can be extended to characterize the degradation of various materials.

Machine Vision–based Detection and Measurement of Welding Scene Features: Developed machine vision-based algorithms to detect geometric measurements of arc welding scene features using Carderock’s patented attenuated-long wave infrared radiation imaging technique.

Innovative traffic-based Artificial Intelligence (AI) and Machine Learning: Develop intelligent systems-based Deep Learning algorithms that can automatically count different objects. Such systems and algorithms are very helpful in designing new smart cities.

Using Machine Learning Algorithms in Prostate Cancer Diagnosis and Prognosis: Various machine learning algorithms and models are used to distinguish benign from malignant prostate cancer.

Internet of Video Things (IoVT): Design video surveillance systems for homeland security applications that match the allowed hardware complexity of the Internet of Video Things (IoVT) infrastructure.

Digital Video Processing Algorithms/Architectures levels: Develop video processing algorithms and architectures. My research involves Video Compression algorithms and architectures, specifically Motion Estimation and Compensation, DCT transform, and Vector Quantization.

VLSI and FPGA Design (Low-Power and High-Speed Performance Embedded Systems): Design video systems while optimizing encoding speed and studying their impact on both area and power consumption. Systems are tested and implemented in either FPGA or ASIC flow design.

Wireless and Digital Communication Systems: Design several techniques and systems to compress the transmitted bit rate of a speech signal over wireless communication channels.