
Mohammed Saha AlamNational University of Singapore | NUS · Department of Biomedical Engineering
Mohammed Saha Alam
Master of Science
About
8
Publications
4,066
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Citations
Introduction
I am a Master's student at the National University of Singapore, specializing in Occupational Health, Safety & Environmental Technology. My research focuses on improving workplace safety and environmental sustainability in biomedical engineering. I aim to integrate innovative technologies with health strategies to enhance occupational safety and diagnostic accuracy, leveraging advancements in fields like cardiology and chemical engineering to achieve better patient outcomes.
Additional affiliations
January 2024 - December 2024
Position
- Research Assistant
Description
- As a Master's student, I focus on enhancing workplace safety and environmental sustainability. My research involves integrating innovative technologies with health strategies, aiming to improve occupational health practices and diagnostic accuracy within the biomedical engineering field.
Education
January 2022 - December 2024
National University of Singapore
Field of study
- Chemical & Biomolecular Engineering
Publications
Publications (8)
Executive Summary Artificial Intelligence has continued redefining modern technology's frontier and penetrating significant markets. The essay highlights some of the biggest and most transformational trends in AI. One of the main areas of change has been AI-driven automation. From the manufacturer to finance, it has changed the job window, thus res...
Pedestrians use mobile devices while using the road. It is dangerous for distracted walking.
Further Literature review on safety at distracted walking. This research reviewed a systematic
literature review on road safety campaigns policies and identified research needs to reduce
accidents. The literature study relevant of government taken steps...
Background The accuracy of coronary computed tomography angiography (CCTA) is sub-optimal in patients with coronary stents. Methods that can increase its diagnostic accuracy are desirable.
Objective A proof-of-concept study was undertaken to determine if corrected coronary opacification (CCO) differences can improve the accuracy of CCTA in stented...
Purpose:
Previous studies have demonstrated that left atrial (LA) volume has incremental prognostic value in predicting major adverse cardiac events (MACE). However, the predictive ability of LA volume in mid diastasis has not been investigated. We determined the incremental predictive value of LA volume indexed to body surface area (LAVi) measure...
Identifying characteristics that predict future adverse events can be used to facilitate patient monitoring or therapy. Although, computed tomographic coronary angiography (CTCA) measures of coronary atherosclerosis, coronary artery disease severity, and left ventricular (LV) ejection fraction are
We sought to quantify coronary artery calcium (CAC) using a single contrast-enhanced cardiac computed tomography angiography (CCTA) study. CCTA has been successfully used for the assessment of coronary artery stenoses, whereas non-contrast ECG-gated computed tomography (Standard-CAC) is commonly performed to quantify CAC. Thus each scan individuall...
-In 2009, the Chalk River nuclear reactor closed for repairs which led to a critical shortage of technetium-99m (Tc-99m). Several centers used thallium-201 (Tl-201) as an alternative radiotracer for myocardial perfusion imaging (MPI). Since Tl-201 is considered by many as a suboptimal radiotracer, we sought to understand the impact of using Tl-201...
Objective
To determine if increased epicardial adipose tissue (EAT) measured by cardiac CT could be associated with impaired myocardial flow reserve (MFR) in patients with non-obstructive coronary artery disease (CAD).
Background
Studies have shown that EAT volume is related to epicardial obstructive CAD, myocardial ischemia and major adverse ca...
Questions
Questions (8)
I am researching the application of AI in improving safety within construction sites, specifically focusing on real-time hazard detection. My objective is to identify:
- AI models or frameworks (e.g., YOLO, TensorFlow) are best suited for detecting hazards such as falling objects or unsafe worker behaviour.
- Methods for integrating these AI tools with existing safety management systems.
- Case studies or examples demonstrating successful implementation of such systems.
I would also like to understand potential challenges, such as computational power requirements, false positives/negatives, and data privacy concerns in deploying these technologies. Any suggestions, research papers, or practical insights would be highly appreciated.
Description:
I am conducting research on the integration of AI in occupational safety and health programs, particularly in high-risk industries like construction and manufacturing. I am interested in understanding:
- The key AI tools or technologies currently used in workplace safety.
- The challenges and limitations organizations face while adopting AI for risk management.
- Case studies or examples of successful AI applications in accident prevention.
Any insights, published research, or practical examples would be greatly appreciated to advance my understanding and contribute to my research.
Hello everyone,
I am researching AI-enabled systems' impact on user interaction and experience. Specifically, I want to understand how different AI technologies (such as computer vision, natural language processing, and machine learning) enhance user engagement and satisfaction in various applications.
Here are a few questions to kick off the discussion:
What are the key factors that influence user interaction with AI-enabled systems?
I’m looking to identify the various elements that affect how users interact with AI systems, such as user interface design, accuracy, reliability, ease of use, and personalization.
How do these systems improve user experience compared to traditional systems?
I am interested in comparing AI-enabled systems with traditional, non-AI systems regarding user experience. How do AI systems provide more intuitive and responsive interactions, offer personalized recommendations, and automate routine tasks?
Are there any notable case studies or research papers that highlight successful implementations of AI in enhancing user interaction?
I would appreciate references to existing research or case studies demonstrating successful AI implementations in improving user interaction. Examples from healthcare, education, or customer service would be precious.
Any insights, references, or personal experiences would be greatly appreciated!
Thank you!
This question seeks references to existing research or case studies that demonstrate successful implementations of AI in improving user interaction. These examples can provide valuable insights and evidence of best practices, helping to inform your research. You might find studies highlighting AI's innovative applications in various fields, such as healthcare, education, or customer service.
Here, you are looking to compare AI-enabled systems with traditional, non-AI systems in terms of user experience. This could involve examining how AI systems can provide more intuitive and responsive interactions, offer personalized recommendations, and automate routine tasks to enhance overall user satisfaction. The goal is to gather insights on the specific advantages that AI brings to user experience.
This question aims to identify the various elements that affect how users interact with AI systems. Factors could include the user interface design, the accuracy and reliability of the AI, the ease of use, and the level of personalization offered. Understanding these factors can help in designing AI systems that are more user-friendly and effective.
I am exploring the potential of AI to transform the teaching of linguistics, specifically for English as a Foreign Language (EFL) students. I am interested in understanding how AI can analyze large amounts of linguistic data and identify patterns or trends beneficial in a pedagogical context. Additionally, I would like to know how AI can assist in personalized learning, automated assessment, and providing interactive exercises. Insights on this area's successful implementations, challenges, and prospects would be highly appreciated.
Artificial intelligence (AI) 's pervasive influence in diverse sectors, including AI components in various computer science subjects, is beneficial, but blending those applications is also crucial. Specialities include programming, databases, human-computer interaction, analysis techniques, and algorithm design.
I am interested in exploring the following:
- Benefits: How can integrating AI into computer science subjects enhance students' interdisciplinary skills and practical application knowledge?
- Implementation: What are the best practices for integrating AI topics across various computer science courses? Are there any successful case studies or examples from universities that have implemented such changes?
- Challenges: What challenges might educators face when integrating AI into existing curricula, and how can they be addressed?
I would greatly appreciate any insights, research papers, case studies, or personal experiences related to integrating AI into computer science education. Thank you!