During my PhD, I developed advanced techniques for predicting and measuring students’ Cognitive Skills (CS), utilizing state-of-the-art cognitive computing methods. My work significantly enhances prediction accuracy by integrating diverse influencing factors and employing advanced modeling approaches.
Key Contributions: __________________________________________________________________________
· This research focused on novel approaches that quantify CS based on factors such as frustration severity, study-related characteristics, and primary student attributes. These techniques substantially improve the accuracy of CS predictions.
· It introduces a novel deep cognitive network that uses Bayesian inference on different nodes to predict student performance. This network has potential applications beyond education, including e-commerce. It also has potential applications in Gamification and Learning Tools that design interactive and educational tools to boost user engagement.
· The novel deep cognitive network simulated nonlinear relationships among CS, frustration, and human factors to enhance prediction accuracy.
Current Research Focus: _____________________________________________________________________
· My current goal is to integrate deep cognitive models with generative AI (GenAI) for processing complex big data in healthcare to improve diagnostics, counter viral diseases, and provide personalized treatment plans. To progress this endeavor, I will leverage my extensive experience in developing deep cognitive models to optimize predictions of student cognitive skills and performance, along with my background in healthcare research and expertise in deep learning, machine learning, and Python.
· My planned research topics include enhancing cloud-based GenAI capabilities to automate data processing and enable self-evolution within a cognitive GenAI framework.
· Additionally, I am focusing on deep cognitive modeling for GenAI to improve healthcare image enhancement.
· Cognitive algorithms enable machines to gain information and make human-level intelligent decisions. These algorithms hold immense potential for advancing intelligent decisions and improving performance in complex tasks.
1. Abd El-Latif, Ahmed A., Yassine Maleh, Mohammed A. El-Affendi, and Sadique Ahmad, eds. Cybersecurity Management in Education Technologies: Risks and Countermeasures for Advancements in E-learning. CRC Press, 2023.
2. Sadique Ahmad et al., “Deep Cognitive Modelling in Remote Sensing Image Processing” https://www.igi-global.com/book/deep-cognitive-modelling-remote-sensing/334227
3. Sadique Ahmad et al., “Navigating Challenges of Object Detection Through Cognitive Computing”, https://www.igi-global.com/book/navigating-challenges-object-detection-through/353988
4. Unpublished Books (in progress):
“Seeing Beyond: The Role of AI in Transforming Medical Imaging”, Springer 2024, Dr. Najib Ben Aoun, Sadique Ahmad, Mohamed Hammad
Editorial Contribution: As a Guest Editor of Special Issues
1. Najib.B. Aoun, Ziliang Ren, Sadique Ahmad, Ridha Ejbali, “Neuro-detection: Advancements in Pattern Detection and Segmentation Techniques in Neuroscience”. https://www.frontiersin.org/research-topics/63238/neuro-detection-advancements-in-pattern-detection-and-segmentation-techniques-in-neuroscience
2. S. Anwar, Sadique Ahmad, S. Joshi, “Harnessing Explainable AI for Precision Cancer Diagnosis and Prognosis”. https://www.frontiersin.org/research-topics/67942/harnessing-explainable-ai-for-precision-cancer-diagnosis-and-prognosis