Publications
Our lab is dedicated to advancing knowledge through rigorous research and impactful scholarship. Below is a curated list of our peer-reviewed journal articles, conference papers, preprints, and other scholarly contributions.
2025
An Interactive AI Solution for Market Research and Report Generation
In the domain of business intelligence, the ability to efficiently synthesize and analyze extensive secondary market research from a plethora of sources represents a critical challenge. This paper outlines the development of an advanced analytical tool that utilizes Large Language Models (LLMs) and ...
In the domain of business intelligence, the ability to efficiently synthesize and analyze extensive secondary market research from a plethora of sources represents a critical challenge. This paper outlines the development of an advanced analytical tool that utilizes Large Language Models (LLMs) and a sophisticated Graphical User Interface (GUI) to streamline the synthesis and analysis of extensive secondary market research. Designed to gather and interpret data from various sources such as news articles, product releases, and professional profiles, the tool provides tailored insights to answer specific business questions. It enables users to interact directly with AI-generated outputs to refine and customize the analysis, ensuring both relevance and accuracy. The tool uniquely supports the creation of detailed market research reports and concise PowerPoint presentations to meet the diverse needs of stakeholders. The paper also discusses the integration of a user feedback loop, which enhances the system’s learning capabilities for continuous performance improvement. The outcome of the paper demonstrates that this tool significantly boosts efficiency, precision, and stakeholder engagement in market research analysis, marking a new phase in human-AI collaboration in business intelligence. Index Terms-Large Language Model, Market Research, Interactive Analytics, Competitive Intelligence.
Dynamic Web Page Modification for Accessibility Using AI and Large Language Models
Web accessibility remains a critical challenge, especially for individuals with disabilities, as most web content is designed without comprehensive adherence to accessibility standards. Current accessibility solutions are limited, relying heavily on the voluntary incorporation of specific design and...
Web accessibility remains a critical challenge, especially for individuals with disabilities, as most web content is designed without comprehensive adherence to accessibility standards. Current accessibility solutions are limited, relying heavily on the voluntary incorporation of specific design and coding standards during web page development. This paper introduces a novel application of artificial intelligence, specifically through the use of Large Language Models (LLMs), to address these challenges. By enabling LLMs to analyze and adaptively modify web page source code, our approach allows for the dynamic standardization of web pages to incorporate user-controllable accessibility features such as text resizing, color contrast adjustments, and font changes. Through the Adaptive User Interface Framework (AUIF), our system adapts digital content in real-time, responding to individual user behaviors and preferences, thereby setting a new standard for accessible user experiences across various digital platforms.
Navigational Assistance for the Blind in Complex Indoor Spaces Using a Vision-Enabled Large Language Model
This study introduces an innovative implementation of a Large Language Model (LLM) that leverages both vision and natural language processing to enhance navigation for individuals who are blind. Unlike traditional methods that rely on pre-existing maps or environmental reconstruction using sensors l...
This study introduces an innovative implementation of a Large Language Model (LLM) that leverages both vision and natural language processing to enhance navigation for individuals who are blind. Unlike traditional methods that rely on pre-existing maps or environmental reconstruction using sensors like LiDAR, our approach requires no prior environmental data and instead utilizes real-time visual cues similar to human navigation strategies. This novel methodology allows the model to dynamically interpret and verbalize complex indoor environments, providing blind users with descriptive audio cues that effectively convey the spatial layout and pertinent features of their surroundings. Conducted in a hospital setting, our experiments demonstrated that this approach significantly improves GPT4-V’s navigation capabilities and offers real-time, contextually relevant guidance, thereby enhancing the independence and safety of blind individuals navigating complex spaces. This research contributes to the understanding of AI’s capabilities in real-world applications and opens new avenues for the deployment of language models in complex, dynamic environments.
Integrating Ethical AI Tools into Educational Practices for Enhancing Academic Integrity
As large language models (LLMs) become increasingly integrated into educational settings, concerns about academic integrity, ethical usage, and student engagement are becoming more prominent. While these AI tools can effectively provide personalized learning experiences and support diverse student n...
As large language models (LLMs) become increasingly integrated into educational settings, concerns about academic integrity, ethical usage, and student engagement are becoming more prominent. While these AI tools can effectively provide personalized learning experiences and support diverse student needs, they also risk overreliance and promote unethical academic practices if used without appropriate safeguards. This paper presents a novel approach that integrates an LLM-based assistant directly into a learning management system (LMS) with carefully designed constraints to encourage active learning, reduce misuse, and preserve academic integrity. We establish core design principles to address the challenges associated with LLMs in education and provide a detailed description of our system’s architecture. Additionally, we conduct a pilot study to assess the tool’s impact on student learning and gather feedback for further improvements. A prototype of the tool is publicly available on Github.
A novel digital twins-driven mutual trust framework for human--robot collaborations
Trust plays an important role and significantly influences human–robot collaborations (HRC). However, most previous research on trust only emphasizes the human attitude toward robots. There needs more understanding of human uncertainties that may also cause disruptions of trust in collaborations. Th...
Trust plays an important role and significantly influences human–robot collaborations (HRC). However, most previous research on trust only emphasizes the human attitude toward robots. There needs more understanding of human uncertainties that may also cause disruptions of trust in collaborations. This paper presents a novel mutual trust framework to provide a relatable vision for future development in HRC from an integrated perspective via the integration of human and robotic digital twins. More specifically, a comprehensive review of current trust research in HRC is first provided, including trust factors and state-of-the-art trust models. Second, a novel human–robot mutual trust framework based on 5-layer digital twins models is introduced. The mutual trust framework highlights the interactions amongst modules of artificial intelligence, simulation, and operation, which can provide wide services in HRC (e.g., task allocation and motion planning). A case study of solving a path planning problem is exemplified to evaluate the performance of the proposed mutual trust framework. Compared with singular trust models, the proposed framework enables robotic systems with real-time response and adaptation to human behavior. Some limitations and future work of the mutual trust framework are elaborated in the end.
2024
Beyond Traditional Teams: Using ChatGPT to Simulate Project Management Dynamics and Software Development in Online Higher Education
In an era characterized by asynchronous online education, effectively teaching project management poses unique challenges. This paper presents a pioneering approach wherein ChatGPT, an advanced conversational AI, is integrated into an undergraduate Systems Analysis & Analytics course to simulate tea...
In an era characterized by asynchronous online education, effectively teaching project management poses unique challenges. This paper presents a pioneering approach wherein ChatGPT, an advanced conversational AI, is integrated into an undergraduate Systems Analysis & Analytics course to simulate team dynamics. Beyond merely emulating interactions, ChatGPT assumes distinct roles-from UI/UX Designers to backend developers-enabling students to experience project management without the logistical complications of coordinating with real team members. We delve further into an exploratory realm, evaluating the feasibility of students collaboratively developing actual software, a Chrome extension in this case, in tandem with ChatGPT. Preliminary feedback suggests an enriching and consistent educational experience, emphasizing the transformative potential of AI-driven agents in pedagogical settings. The paper sets the stage for a broader discussion on the future of education, inviting readers to consider the implications of AI not just as a tool, but as a collaborator in teaching, learning and development.
Bridging the Accessibility Gap in Online Shopping with AI-Driven Solutions
Despite advancements in assistive technologies, blind and low vision (BLV) individuals continue to face significant challenges in online shopping, particularly with interpreting visual content and comparing products. Existing tools often lack seamless integration and intuitive interaction, hindering...
Despite advancements in assistive technologies, blind and low vision (BLV) individuals continue to face significant challenges in online shopping, particularly with interpreting visual content and comparing products. Existing tools often lack seamless integration and intuitive interaction, hindering an equitable shopping experience. This paper introduces Shop Sight, a Chrome browser extension designed to enhance online shopping accessibility for BLV users. Shop Sight leverages artificial intelligence (AI) and voice-activated capabilities to generate context-rich product image descriptions and to facilitate simplified product comparisons through voice commands. The development and evaluation of Shop Sight demonstrate its potential to bridge the gap between current AI capabilities and the real-world needs of BLV users in e-commerce. By providing AI-generated image descriptions and voice-activated product comparisons, Shop Sight empowers BLV individuals with greater independence and a more personalized, efficient online shopping experience. Future work will focus on refining the tool's features, incorporating user feedback, and addressing identified limitations to further enhance its usability and effectiveness.
Enhancing healthcare user interfaces through large language models within the adaptive user interface framework
In the pursuit of enhancing digital user experiences within healthcare, this research investigates the novel application of Large Language Models (LLMs) in the Adaptive User Interface Framework (AUIF). This framework aims to redefine user interaction by providing real-time, personalized interface ad...
In the pursuit of enhancing digital user experiences within healthcare, this research investigates the novel application of Large Language Models (LLMs) in the Adaptive User Interface Framework (AUIF). This framework aims to redefine user interaction by providing real-time, personalized interface adjustments. By systematically applying LLMs to user interface enhancement, the AUIF addresses the static nature of current digital health platforms, offering a dynamic and adaptive alternative that responds to individual user behaviors and preferences. This study explores the pioneering integration of LLMs for user experience (UX) improvement recommendations and real-time HyperText Markup Language (HTML) content adjustments, marking a significant step forward in intelligent user interface design. The implications of this research are vast, with the potential to improve patient engagement, and satisfaction, and to address the pressing need for interfaces that adapt to diverse user behaviors and preferences.
Enhancing telehealth patient experience with emotion-sensitive large language models
This study explores the integration of Large Language Models (LLMs), specifically ChatGPT-4, to improve patient experience in telehealth. Addressing the challenge of patient anxiety during waiting periods, we implemented ChatGPT-4 for real-time emotion detection and dynamic background generation. Ex...
This study explores the integration of Large Language Models (LLMs), specifically ChatGPT-4, to improve patient experience in telehealth. Addressing the challenge of patient anxiety during waiting periods, we implemented ChatGPT-4 for real-time emotion detection and dynamic background generation. Experiments using the FACES database and qualitative feedback on generated backgrounds show that ChatGPT-4 can accurately identify emotions and create calming visual environments. These findings suggest that LLMs can significantly enhance patient engagement and satisfaction. Future developments should focus on personalized AI training and real-time adaptive systems for a more nuanced approach to patient care in telehealth.
Enhancing throughput in hyperledger fabric through endorsement policy strategy
In the realm of private permissioned blockchain platforms, increasing throughput is a pivotal objective. This paper focuses on the optimization of throughput in Hyperledger Fabric, a private permissioned leading blockchain framework tailored for enterprise applications. The paper proposes a novel ap...
In the realm of private permissioned blockchain platforms, increasing throughput is a pivotal objective. This paper focuses on the optimization of throughput in Hyperledger Fabric, a private permissioned leading blockchain framework tailored for enterprise applications. The paper proposes a novel approach to enhancing the platform’s performance by reevaluating the endorsement policy. By implementing a Less-Than-Half endorsement policy, the paper aims to streamline transaction validation processes and bridge the gap between Fabric’s throughput and the demands of large-scale industrial applications. The proposed method objects to boost transaction throughput without compromising security or reliability. The paper provides an overview of Hyperledger Fabric architecture, discusses the pre-verification mechanism, and presents the proposed method for optimizing throughput. The performance of the system is analyzed using Hyperledger Caliper and Prometheus. Simulation results show increase in the throughput of the Less-Than-Half of the endorsement policy as compared to the majority and it also demonstrates the significant reduction in the latency of the Less-Than-Half endorsement policy.
PentaPen: Combining Penalized Models to Identify Important SNPs on Whole-genome Arabidopsis thaliana Data
In the rapidly advancing field of genomics, the identification of Single Nucleotide Polymorphisms (SNPs) plays a crucial role in understanding complex phenotypic traits. This study introduces “PentaPen”, an innovative computational workflow which combines the strengths of five penalized models to ac...
In the rapidly advancing field of genomics, the identification of Single Nucleotide Polymorphisms (SNPs) plays a crucial role in understanding complex phenotypic traits. This study introduces “PentaPen”, an innovative computational workflow which combines the strengths of five penalized models to achieve improved accuracy in SNP detection. We compare the performance of PentaPen with existing models, highlighting its advantages in solving problems arising from when the number of predictors exceeds the number of samples. Beyond model comparison, we provide insights into PentaPen’s effectiveness in utilizing all SNPs as input, streamlines data pre-processing, and leverages parallel computation, enabling the workflow a considerable stride in SNP detection. Furthermore, a thorough evaluation and comparison of computational complexities signifies competitive edge of the workflow over individual penalized models. As future research directions, we propose applications of PentaPen to plant-specific characteristics and suggest further explorations to assess the robustness of its findings. In summary, this manuscript presents the genomics community with a tool that combines computational efficiency with high-precision SNP detection, making a strong contribution to the field of genomic research.
SmartCaption AI-Enhancing Web Accessibility with Context-Aware Image Descriptions Using Large Language Models
The Internet provides vast amounts of information, services, and products. However, blind individuals and those with severe vision impairments face significant challenges in navi-gating web content, especially with understanding images. This paper introduces SmartCaption AI, an innovative solution t...
The Internet provides vast amounts of information, services, and products. However, blind individuals and those with severe vision impairments face significant challenges in navi-gating web content, especially with understanding images. This paper introduces SmartCaption AI, an innovative solution that leverages Large Language Models (LLM) to generate descriptive text for images on web pages. By summarizing the content of a web page, SmartCaption AI provides relevant context for the LLM to produce accurate and meaningful image descriptions. These descriptions are seamlessly integrated into the web page's structure, allowing text- to-speech software to read them aloud to visually impaired users. SmartCaption AI offers several key contributions to web accessibility. It ensures the generated descriptions are contextually relevant, enhances the browsing experience by integrating real-time descriptions, and provides a universally accessible solution through a Chrome extension. This approach addresses the critical issue of missing or inadequate alternative text for images, thereby bridging the digital divide between sighted and visually impaired individuals. The results of our experiment demonstrated the effectiveness of SmartCaption AI, with an average score of 8.3/10, significantly outperforming state-of-art solutions: ImageToText (1.7/10) and AI-MCS (3.6/10). The source code of the tool is available on GitHub.
Tracing Economic Vibrancy: AI-Driven Analysis of Geographic Clustering in Legal Businesses
Geographic clustering of businesses holds significant importance in understanding local economic dynamics, identifying areas of commercial activity, and assisting in spatial analysis for economic development. Artificial intelligence (AI) driven analysis is employed in this paper to investigate patte...
Geographic clustering of businesses holds significant importance in understanding local economic dynamics, identifying areas of commercial activity, and assisting in spatial analysis for economic development. Artificial intelligence (AI) driven analysis is employed in this paper to investigate patterns of geographic clustering, particularly focusing on legal businesses within a given area. Data extraction techniques help preprocess business directories and classification codes to aggregate business addresses and visualize their spatial distribution. Clustering algorithms are used in conjunction with Geographic Information System (GIS) tools for data visualization and precise mapping, with respect to economic indicators. Expected outcomes include generating geographical distribution maps, comparing clustering algorithm results, and insight into urban business clustering patterns. This research considers potential external factors influencing business agglomeration and data currency. Recommendations focus on integrating AI-driven analysis with GIS tools and future research domains. Overall, this paper highlights the intersection of AI and geospatial analysis, providing stakeholders with valuable insights into the spatial distribution of economic activities within a target area.
Transforming patient experience in underserved areas with innovative voice-based healthcare solutions
This study presents a novel voice-driven online appointment system aimed at improving healthcare access in rural and indigenous communities. Utilizing existing telephone infrastructure, the system transcends the limitations of traditional chatbots by leveraging Large Language Models (LLMs) and advan...
This study presents a novel voice-driven online appointment system aimed at improving healthcare access in rural and indigenous communities. Utilizing existing telephone infrastructure, the system transcends the limitations of traditional chatbots by leveraging Large Language Models (LLMs) and advanced voice recognition technologies like Whisper. This approach enables the transformation of conventional phone calls into a streamlined digital booking process. The system’s integration with current booking processes in rural healthcare facilities and facilitates a smooth transition from voice-based to digital scheduling, providing an intuitive and efficient user experience. This innovation addresses critical healthcare accessibility challenges, notably reducing appointment booking barriers and enhancing the overall patient experience. The implementation of this technology in underserved areas demonstrates a significant advancement in patient-centered care, highlighting the role of LLMs in harmonizing traditional and digital healthcare practices. The paper provides a comprehensive overview of the system’s development, explores the challenges that may encounter, and discusses the significant potential of this approach in shaping future healthcare technologies and influencing policy decisions in healthcare accessibility.
2023
Privacy, security and resilience in mobile healthcare applications
With the advent of mobile applications in health service systems, concerns such as security, privacy, usability, and resilience have been raised. We developed a system view of the concepts of security, privacy, resilience along with their relationship, and proposed a set of principles for designing ...
With the advent of mobile applications in health service systems, concerns such as security, privacy, usability, and resilience have been raised. We developed a system view of the concepts of security, privacy, resilience along with their relationship, and proposed a set of principles for designing a mobile application linking the resilience and security in privacy protection. Such study was not found in literature before. This system's view of privacy, security, and resilience has laid a foundation to develop a more effective service system. A case study is presented to illustrate how the proposed principles work in a mobile healthcare application.
A semantic model for enterprise application integration in the era of data explosion and globalisation
This paper presents a model for Enterprise Application Integration (EAI) in the modern era of data explosion and globalisation. Application here refers to software, which is in essence data system, and data refers to both information and knowledge (data serves as a vehicle for information as well as...
This paper presents a model for Enterprise Application Integration (EAI) in the modern era of data explosion and globalisation. Application here refers to software, which is in essence data system, and data refers to both information and knowledge (data serves as a vehicle for information as well as knowledge). The salient features of the model are: (1) separation of business functions from applications and enterprises, (2) three-layer architecture of the model (conceptual or semantic level, external or application level, internal or realisation level), and (3) integration of structured, semi-structured and non-structured data. To our best knowledge, the existing model or solution to EAI does not hold all the three features. A case study is presented to illustrate how the model works. The model can be used by an individual enterprise or a group of enterprises that form a network, e.g., a holistic supply chain network.
A novel scheduling method for reduction of both waiting time and travel time of patients to visit health care units in the case of mobile communication
This paper proposes a new scheduling problem for patient visits with two objectives: minimizing patient waiting time and travel time. It also presents a novel encoding method for Genetic Algorithms (GA) that is well-suited for this problem. Experiments demonstrate that the proposed encoding method r...
This paper proposes a new scheduling problem for patient visits with two objectives: minimizing patient waiting time and travel time. It also presents a novel encoding method for Genetic Algorithms (GA) that is well-suited for this problem. Experiments demonstrate that the proposed encoding method reduces optimization iterations by 17% compared to conventional methods, and the GA can decrease waiting time by up to 58.2% and travel time by up to 89.3% for specific examples. The novel scheduling problem and the encoding method are two main contributions of this work.
Adaptive user interface framework powered by a large language model for culturally sensitive virtual healthcare applications
In this research, we propose the development of anAdaptive User Interface (UI) Framework for virtual healthcareapplications, powered by a Large Language Model (LLM). Theintention is to revolutionize the way healthcare services arerendered by creating a real-time responsive system that catersto diver...
In this research, we propose the development of anAdaptive User Interface (UI) Framework for virtual healthcareapplications, powered by a Large Language Model (LLM). Theintention is to revolutionize the way healthcare services arerendered by creating a real-time responsive system that catersto diverse patient needs. Unlike conventional healthcareapplications, this framework utilizes various sensors andinteractive inputs to continuously adapt to users' feedback. Itharnesses the potential of deep learning to process this feedbackand make culturally sensitive adaptations, ensuring morepersonalized and effective care for Indigenous, Black, andPeople of Colour (IBPOC) populations. A unique aspect of thissystem is that its adaptations are not predetermined; instead, itdynamically generates changes based on the user feedbackanalyzed by the LLM. To demonstrate the efficacy of thisframework, a demo healthcare application is being developed.We expect this initiative to significantly contribute to the field ofvirtual healthcare by introducing a more inclusive, personalized,and adaptive platform, ultimately leading to improved patientcare outcomes.
Optimizing healthcare resource allocation through digital twins: a multi-objective approach for efficiency, equity, and resilience
Leveraging digital twins, this study presents an innovative approach to healthcare resourceallocation, emphasizing efficiency, equity, and resilience. Traditional methods often centralizeresources, disadvantaging rural areas. Our model, rooted in digital twin principles, addresses this byoptimizing ...
Leveraging digital twins, this study presents an innovative approach to healthcare resourceallocation, emphasizing efficiency, equity, and resilience. Traditional methods often centralizeresources, disadvantaging rural areas. Our model, rooted in digital twin principles, addresses this byoptimizing patient accessibility to services. Validated through a case study on COVID-19 test siteallocation in Saskatchewan, Canada, our approach can reduce testing disruptions by up to 92% if asite becomes inoperative. Beyond testing, the model aids in allocating critical healthcare resources,such as ICU beds and medications. While focused on healthcare, the methodology offers broaderresource allocation implications, marking a pioneering step in combining equity and resilience.
2021
Human factors among workers in a small manufacturing enterprise: a case study
In most small manufacturing enterprise (SME), production planning and scheduling are crucial operational management tasks required. A typical make-to-order company is plagued by frequent absenteeism and abrupt resignation of skilled workers, leading to an increase in late delivery of jobs and rework...
In most small manufacturing enterprise (SME), production planning and scheduling are crucial operational management tasks required. A typical make-to-order company is plagued by frequent absenteeism and abrupt resignation of skilled workers, leading to an increase in late delivery of jobs and rework of returned jobs. The effects of some human factors, for example, job skill, job satisfaction, and job fatigue to mention a few were studied using statistical analysis. It was concluded that human factors may have significant effects on job performance in a SME. To our best knowledge, this conclusion is less known in enterprise systems in general.
2018
On rapid prototyping of assembly systems--a modular approach
This paper proposes a new product and manufacturing technology for rapid prototyping of product systems (RPPSs). It is noted that a system in this paper is defined as a physical assembly that can be decomposed into components. The rapid prototyping is achieved by a novel modular concept, that is, th...
This paper proposes a new product and manufacturing technology for rapid prototyping of product systems (RPPSs). It is noted that a system in this paper is defined as a physical assembly that can be decomposed into components. The rapid prototyping is achieved by a novel modular concept, that is, the base materials to build a component as well as an assembly is highly modularised (the interface between any two modules are identical) and building a system is simply by assembling the modules. The rapid prototyping in this paper differs significantly from the rapid prototyping in literature in that the latter builds a system layer by layer and further primarily builds a component instead of assembly (building of an assembly is actually very limited with the latter, though possible). This paper explains the RPPS approach and presents a feasibility study on the RPPS technology. It has been shown that the RPPS technology is promising.