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Welcome to IgMin Research – an Open Access journal uniting Biology, Medicine, and Engineering. We’re dedicated to advancing global knowledge and fostering collaboration across scientific fields.
At IgMin Research, we bridge the frontiers of Biology, Medicine, and Engineering to foster interdisciplinary innovation. Our expanded scope now embraces a wide spectrum of scientific disciplines, empowering global researchers to explore, contribute, and collaborate through open access.
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Biography
Dr. Qazi Waqas Khan is a dedicated and accomplished researcher in the field of Computer Engineering, currently serving as a Ph.D. candidate at Jeju National University, Republic of Korea . He received his prestigious Fulbright Ph.D. Scholarship in September 2022, following a fully merit‑based M.S. in Computer Science from COMSATS University Islamabad.
Affiliated with the Department of Computer Engineering, Dr. Khan focuses on leveraging machine learning, particularly within robotics, signal processing, and federated learning domains. His scholarly contributions include leading authorship on several notable publications:
A Machine Learning–based Method for COVID‑19 and Pneumonia Detection (IgMin Research, 2024), where he designed and evaluated CNN‑based and hybrid models (CNN+SVM, CNN+RF, CNN+XGBoost) achieving 99.47% recall for pneumonia detection and 95.45% accuracy for COVID‑19 diagnosis from chest X‑rays.
Exploring Markov Decision Processes: A Comprehensive Survey of Optimization Applications and Techniques (IgMin Research, 2024), which provides an extensive survey of MDP usage in optimization across diverse engineering fields.
Electrical Vehicle Charging Event Classification Using Machine Learning, expanding his research into sustainable energy systems and real‑world signal classification problems
Additional studies on diabetes prediction and loan status automation, highlighting his versatility in both healthcare and financial machine learning applications .
Dr. Khan’s work demonstrates a commitment to innovation in applied AI—bridging deep learning techniques with practical engineering problems. His research garners wide visibility, reflected in strong publication metrics and diverse citations across interdisciplinary domains. As he progresses through his Ph.D., Dr. Khan continues to explore novel algorithmic frameworks in federated and reinforcement learning, with the vision of advancing intelligent systems capable of real‑world impact.
Research Interest
Dr. Qazi Waqas Khan's research interests lie at the intersection of artificial intelligence, machine learning, and computer engineering, with a strong focus on practical applications in healthcare, robotics, and intelligent systems. His work explores deep learning architectures such as convolutional neural networks (CNNs) for medical image classification, federated learning for privacy-preserving data modeling, and reinforcement learning frameworks like Markov Decision Processes (MDPs) for adaptive decision-making in real-time environments. Dr. Khan is particularly interested in the development of hybrid AI models that integrate machine learning with statistical optimization to enhance accuracy and scalability. His recent studies address COVID-19 and pneumonia detection using X-ray images, electric vehicle event classification, and automated risk prediction in financial and healthcare datasets. As part of his Ph.D. at Jeju National University, Korea, he continues to investigate robust, explainable AI techniques aimed at solving real-world challenges in smart healthcare, sustainable energy, and autonomous systems.
Open Access Policy refers to a set of principles and guidelines aimed at providing unrestricted access to scholarly research and literature. It promotes the free availability and unrestricted use of research outputs, enabling researchers, students, and the general public to access, read, download, and distribute scholarly articles without financial or legal barriers. In this response, I will provide you with an overview of the history and latest resolutions related to Open Access Policy.
Pneumonia is described as an acute infection of lung tissue produced by one or more bacteria, and Coronavirus Disease (COVID-19) is a deadly virus that affects the lungs of the human body. The symptoms of COVID-19 disease are closely related to pneumonia. In this work, we identify the patients of pneumonia and coronavirus from chest X-ray images. We used a convolutional neural network for spatial feature learning from X-ray images. We experimented with pneumonia and coronavirus X-ray images in the Kaggle dataset. Pneumonia and corona patients a...re classified using a feed-forward neural network and hybrid models (CNN+SVM, CNN+RF, and CNN+Xgboost). The experimental findings on the Pneumonia dataset demonstrate that CNN detects Pneumonia patients with 99.47% recall. The overall experiments on COVID-19 x-ray images show that CNN detected the COVID-19 and pneumonia with 95.45% accuracy.
Open Access Policy refers to a set of principles and guidelines aimed at providing unrestricted access to scholarly research and literature. It promotes the free availability and unrestricted use of research outputs, enabling researchers, students, and the general public to access, read, download, and distribute scholarly articles without financial or legal barriers. In this response, I will provide you with an overview of the history and latest resolutions related to Open Access Policy.
Markov decision process is a dynamic programming algorithm that can be used to solve an optimization problem. It was used in applications like robotics, radar tracking, medical treatments, and decision-making. In the existing literature, the researcher only targets a few applications area of MDP. However, this work surveyed the Markov decision process’s application in various regions for solving optimization problems. In a survey, we compared optimization techniques based on MDP. We performed a comparative analysis of past work of other r...esearchers in the last few years based on a few parameters. These parameters are focused on the proposed problem, the proposed methodology for solving an optimization problem, and the results and outcomes of the optimization technique in solving a specific problem. Reinforcement learning is an emerging machine learning domain based on the Markov decision process. In this work, we conclude that the MDP-based approach is most widely used when deciding on the current state in some environments to move to the next state.
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