arXiv subjects

Muhammad Raees

Publications and source records attributed to Muhammad Raees.

10 recordsLinked to original sources

arXiv2026

An Analytical Multiple Criteria Framework for Temporal and Dynamic Business-to-Business Customer Segmentation in Manufacturing

In sales and marketing, customer segmentation is an important tool for formulating strategies for customer treatment and supply chain management. Most segmentation implementations rely on limited criteria, such as recency, frequency, and monetary (RFM) modeling, which often fail to capture complex business interactions. In this work, we design and evaluate a dynamic multi-criteria decision-making (MCDM) method in a business-to-business (B2B) manufacturing context by 1) extending RFM to dimensions of stability and growth, 2) integrating an adaptive and analytical hierarchical process to match business objectives, and 3) evaluating multivariate time-series clustering models. We then measure customer stability, tracking between-segment transitions, and volatility over time, and apply a graph-based consensus model to further strengthen the analysis. We test the efficacy of the proposed method using a real-world manufacturing company dataset to segment more than 3,000 B2B customers, showing strong robustness to temporal shifts. The implementation enables domain experts with preferential analytics to devise their strategies, providing effective decision support for B2B customer segmentation.

cs.LG↗

arXiv2026

From Trust to Appropriate Reliance: Measurement Constructs in Human-AI Decision-Making

While human-AI decision-making research has primarily used trust measurements to assess the practical usage of AI systems by their end-users, recent empirical evidence suggests that trust measurements do not inform users' appropriate reliance on AI systems. While examining the human-AI decision-making literature, in this work, we review empirical studies that assess people's appropriate reliance on AI advice, differentiating measurements and constructs of appropriate reliance from trust and mere reliance. Our analysis of literature shows that constructs for human-AI appropriate reliance are still fragmented in research. We present three views on appropriate reliance, namely Traditional, Appropriateness, and Dominance, as discussed in research. Using these views, we evaluate objective metrics reported in studies and argue for their consensus to facilitate the comparison across empirical research. We also discuss how studies employ objective metrics and examine their validity in application contexts. Our work contributes to the critical body of research on exploring objective metrics for assessing humans' appropriate reliance on AI advice.

cs.HC↗

arXiv2026

Gendered Digital Financing Adoption and Women's Financial Inclusion in Pakistan

Financial inclusion is a longstanding concern across underdeveloped communities, particularly for women. However, there are limited data-driven measures to first quantitatively identify such concerns and second to inform policies. In this work, we examine the digital money service adoption and women's financial inclusion in the context of Pakistan. We use the nationally representative Global Findex data from the World Bank to analyze how mobile money usage, when moderated by phone ownership, internet access, and education, affects women's access to formal financial services. Our findings show that women who adopt mobile money services have significantly higher odds of accessing formal financial systems. Findings also reveal nuanced insights: internet access does not significantly impact inclusion, highlighting the influence of socio-cultural constraints. Despite the limitations of using cross-sectional data and the absence of qualitative dimensions, our study contributes empirical evidence on gendered digital finance adoption. The findings have important implications for policy, including the need for women-centric fintech design and digital literacy reforms to bridge the gender gap in financial inclusion.

cs.CY↗

arXiv2026

How University Disability Services Professionals Write Image Descriptions for HCI Figures Using Generative AI

Disability Services Office (DSO) professionals at higher education institutions write alt text for {visual content}. However, due to the complexity of visual content, such as HCI figures in research publications, DSO professionals can struggle to write high-quality alt text if they lack subject expertise. Generative AI has shown potential in understanding figures and writing their descriptions, yet its support for DSO professionals is underexplored, and limited work evaluates the quality of alt text generated with AI assistance. In this work, we conducted two studies: first, we investigated generative AI support for writing alt text for HCI figures with 12 DSO professionals. Second, we recruited 11 HCI experts to evaluate the alt text written by DSO professionals. Findings show that alt text written solely by DSO professionals has lower quality than alt text written with AI assistance. AI assistance also helped DSO professionals write alt text more quickly and with greater confidence; however, they reported inefficiencies in interactions with the AI. Our work contributes to exploring AI support for non-subject expert accessibility professionals.

cs.HC↗

arXiv2025

Object Oriented-Based Metrics to Predict Fault Proneness in Software Design

In object-oriented software design, various metrics predict software systems' fault proneness. Fault predictions can considerably improve the quality of the development process and the software product. In this paper, we look at the relationship between object-oriented software metrics and their implications on fault proneness. Such relationships can help determine metrics that help determine software faults. Studies indicate that object-oriented metrics are indeed a good predictor of software fault proneness, however, there are some differences among existing work as to which metric is most apt for predicting software faults.

cs.SE↗

arXiv2025

UX Challenges in Implementing an Interactive B2B Customer Segmentation Tool

In our effort to implement an interactive customer segmentation tool for a global manufacturing company, we identified user experience (UX) challenges with technical implications. The main challenge relates to domain users' effort, in our case sales experts, to interpret the clusters produced by an unsupervised Machine Learning (ML) algorithm, for creating a customer segmentation. An additional challenge is what sort of interactions should such a tool support to enable meaningful interpretations of the output of clustering models. In this case study, we describe what we learned from implementing an Interactive Machine Learning (IML) prototype to address such UX challenges. We leverage a multi-year real-world dataset and domain experts' feedback from a global manufacturing company to evaluate our tool. We report what we found to be effective and wish to inform designers of IML systems in the context of customer segmentation and other related unsupervised ML tools.

cs.HC↗

arXiv2024

Lexicon-Based Sentiment Analysis on Text Polarities with Evaluation of Classification Models

Sentiment analysis possesses the potential of diverse applicability on digital platforms. Sentiment analysis extracts the polarity to understand the intensity and subjectivity in the text. This work uses a lexicon-based method to perform sentiment analysis and shows an evaluation of classification models trained over textual data. The lexicon-based methods identify the intensity of emotion and subjectivity at word levels. The categorization identifies the informative words inside a text and specifies the quantitative ranking of the polarity of words. This work is based on a multi-class problem of text being labeled as positive, negative, or neutral. Twitter sentiment dataset containing 1.6 million unprocessed tweets is used with lexicon-based methods like Text Blob and Vader Sentiment to introduce the neutrality measure on text. The analysis of lexicons shows how the word count and the intensity classify the text. A comparative analysis of machine learning models, Naiive Bayes, Support Vector Machines, Multinomial Logistic Regression, Random Forest, and Extreme Gradient (XG) Boost performed across multiple performance metrics. The best estimations are achieved through Random Forest with an accuracy score of 81%. Additionally, sentiment analysis is applied for a personality judgment case against a Twitter profile based on online activity.

cs.CL↗

arXiv2024

Context-aware Advertisement Modeling and Applications in Rapid Transit Systems

In today's businesses, marketing has been a central trend for growth. Marketing quality is equally important as product quality and relevant metrics. Quality of Marketing depends on targeting the right person. Technology adaptations have been slow in many fields but have captured some aspects of human life to make an impact. For instance, in marketing, recent developments have provided a significant shift toward data-driven approaches. In this paper, we present an advertisement model using behavioral and tracking analysis. We extract users' behavioral data upholding their privacy principle and perform data manipulations and pattern mining for effective analysis. We present a model using the agent-based modeling (ABM) technique, with the target audience of rapid transit system users to target the right person for advertisement applications. We also outline the Overview, Design, and Details concept of ABM.

cs.MA↗

arXiv2024

Context-Aware Agent-based Model for Smart Long Distance Transport System

Long-distance transport plays a vital role in the economic growth of countries. However, there is a lack of systems being developed for monitoring and support of long-route vehicles (LRV). Sustainable and context-aware transport systems with modern technologies are needed. We model for long-distance vehicle transportation monitoring and support systems in a multi-agent environment. Our model incorporates the distance vehicle transport mechanism through agent-based modeling (ABM). This model constitutes the design protocol of ABM called Overview, Design, and Details (ODD). This model constitutes that every category of agents is offering information as a service. Hence, a federation of services through protocol for the communication between sensors and software components is desired. Such integration of services supports monitoring and tracking of vehicles on the route. The model simulations provide useful results for the integration of services based on smart objects.

cs.MA↗

arXiv2024

From Explainable to Interactive AI: A Literature Review on Current Trends in Human-AI Interaction

AI systems are increasingly being adopted across various domains and application areas. With this surge, there is a growing research focus and societal concern for actively involving humans in developing, operating, and adopting these systems. Despite this concern, most existing literature on AI and Human-Computer Interaction (HCI) primarily focuses on explaining how AI systems operate and, at times, allowing users to contest AI decisions. Existing studies often overlook more impactful forms of user interaction with AI systems, such as giving users agency beyond contestability and enabling them to adapt and even co-design the AI's internal mechanics. In this survey, we aim to bridge this gap by reviewing the state-of-the-art in Human-Centered AI literature, the domain where AI and HCI studies converge, extending past Explainable and Contestable AI, delving into the Interactive AI and beyond. Our analysis contributes to shaping the trajectory of future Interactive AI design and advocates for a more user-centric approach that provides users with greater agency, fostering not only their understanding of AI's workings but also their active engagement in its development and evolution.

cs.HC↗