Digital Commons Network^™

Impact Of Feature Standardization On Heart Disease Prediction A Comparative Analysis Of Logistic Regression And Support Vector Machine Models, Norsyela Muhammad Noor Mathivanan, Eric Foo Zhi Xian, Debbie Foo Yong, Hiang Kiat Chua

Malaysian Journal of Computing (MJoC)

Cardiovascular diseases are among the leading causes of global mortality. Heart disease, in particular, remains a major contributor to this burden, highlighting the need for effective predictive models to enable early detection. This study investigates the impact of feature standardization using StandardScaler on the performance of two prominent machine learning models involving Logistic Regression (LR) and Support Vector Machine (SVM) for predicting heart disease. The research utilizes a dataset comprising demographic and clinical attributes of patients, focusing on the role of feature standardization in enhancing model performance. The study compares models trained on raw data and standardized data, applying performance …

Haar Cascade Algorithm For Microsleep Detection, Norkhushaini Awang, Ahmad Mirza Azhar

Malaysian Journal of Computing (MJoC)

Drowsy driving, particularly due to microsleep episodes, is a significant cause of traffic accidents, with existing solutions being often too costly or limited for widespread adoption. This project addresses this critical gap by developing a cost-effective, real-time Internet of Things (IoT)-based anti-microsleep alarm system. The system's development followed a fourstage process: Planning, Design, Development, and Evaluation. During the development phase, the system was built in Python using OpenCV and dlib for real-time facial analysis and the Haar Cascade algorithm for efficient facial feature detection. Key metrics like the Eye Aspect Ratio (EAR) and lip distance were monitored to identify signs …

Review And Gap Analysis On Mathematical Programming Models For Urban E Grocery Delivery, Nur Hazimah Basir, Adibah Shuib

Malaysian Journal of Computing (MJoC)

Online grocery shopping or e-grocery has becoming more relevant nowadays when consumers' shopping habit changed due to pandemic COVID-19 while e-Commerce rapidly transformed consumers’ lifestyle and buying behaviour in recent years, Consumers’ expectation for faster, better and cheaper delivery put e-grocers under rising pressure to improve delivery speed, achieving environmentally friendly delivery methods and addressing issues of making profit. There have been studies worldwide on development of more efficient e-grocery delivery system. However, studies concerning e-grocery delivery in Malaysia are still lacking especially on those utilizing mathematical programming models for delivery optimization. Our study focuses on the formulation of mixed …

E-Commerce Requirement Analysis And Design With Fogg Behavior Model Fbm, Fauziah Ahmad, Syafiyana Elysha Asmadi, Norjansalika Janom

Malaysian Journal of Computing (MJoC)

E-commerce platforms often struggle to convert user visits into meaningful actions, and this paper explores how the Fogg Behavior Model (FBM) can be used to design a more effective e-commerce (EC) platform. FBM focuses on three key factors that influence user behavior: motivation, ability, and triggers. It offers a structured framework for designing systems that not only meet functional needs but also enhance user engagement and actions. The Web Development Life Cycle (WDLC) method was employed to develop an EC platform for Carro Empire, a small business enterprise that wanted to improve its EC platform to attract and expand its …

Hamming Distance Of Generalised L-R Intuitionistic Fuzzy Number And Its Application, Muhammad Asyran Shafie, Nor Hanimah Kamis, Daud Mohamad, Seripah Awang Kechil

Malaysian Journal of Computing (MJoC)

This paper proposes a distance measure for generalised L-R intuitionistic fuzzy number (GLRIFN) which is Hamming distance, aiming to enhance the theoretical and practical tools available for decision-making under uncertainty. The properties of the Hamming distance of generalised L-R intuitionistic fuzzy number are also discussed in this study. GLRIFN extends traditional L-R intuitionistic fuzzy number by incorporating confidence level for both membership and non-membership functions, making them more reliable in the evaluation process. To demonstrate the practical utility of the proposed measure, it is applied within the Generalised L-R Intuitionistic Fuzzy Technique for Order of Preference by Similarity to Ideal …

A Multicriteria Decision Model For Sustainable Port Functions In Environmental Perspectives

Malaysian Journal of Computing (MJoC)

Port operations, while critical to global trade and economic growth, contribute significantly to environmental pressures through activities such as vessel traffic, cargo handling, dredging, and on-dock transportation, leading to emissions of greenhouse gases and other pollutants. Hence, it is of priority to have a systematic evaluation and prioritization of sustainability measures across port functions. This study employs a multi-criteria decision-making (MCDM) approach, the Analytic Hierarchy Process (AHP), to evaluate and prioritize key functional roles and subfunctions that contribute to port sustainability performance. By engaging expert judgment from two leading ports in Malaysia, the analysis identifies stakeholder priorities on the emphasis …

Utilizing Mathematical Software For The Teaching And Learning Of Mathematics In Malaysia, Naiim Fatimi Che Mansor, Saufianim Jana Aksah, Nor Aziyatul Izni, Khairul Huda Yusof, M. N. Mohammed

Malaysian Journal of Computing (MJoC)

The paper examines the effects of mathematical software on the academic performance of secondary school students, along with its roles in facilitating technological adaptation among the students in mathematics education. The study is motivated by persistent challenges in students’ mathematics achievement and the limited integration of technology in classroom practices, despite the increasing availability of digital tools in schools. Two main objectives guided the research are to evaluate the effectiveness of mathematical software in improving students’ comprehension of mathematics, and to measure the extent of technology integration among students in mathematics learning. A quantitative research design was employed, involving 100 …

Designing A Community Based Information System Using Activity Theory A Case Study In Taman Lapangan Jaya, Norsyazwani Hafizah Reduan, Kamalia Azma Kamaruddin

Malaysian Journal of Computing (MJoC)

Effective communication and information sharing are vital for active neighbourhood participation, yet many communities still rely on informal group chats that lead to fragmented updates, lost documents, and frequent miscommunication. This study addresses this gap by applying Activity Theory (AT) to systematically identify requirements for a centralized community communication and information-sharing platform in Taman Lapangan Jaya. Using AT, the seven elements of activity were mapped to reveal key issues such as the communication bottleneck, lack of document accessibility, and unclear division of responsibilities. These findings informed the design of a web-based system featuring structured document repositories, role-based information management, and …

Human-Computer Interaction Experience And Customer Loyalty In Chinese Hotels The Moderating Effect Of Brand Trust, Xie Lu, Xinying Yuan, Zeyu Chen, Johanudin Lahap

Malaysian Journal of Computing (MJoC)

In the era of digital transformation, human-computer interaction has become a critical factor in shaping customer experiences and loyalty, particularly in the hospitality industry. This study investigates the relationship between human-computer interaction experiences and customer loyalty in Chinese hotels, with a focus on the moderating role of brand trust. Drawing on the Technology Acceptance Model, the research explores how perceived ease of use and perceived usefulness of human-computer interaction systems influence customer loyalty while considering the impact of brand trust as a moderating variable. Using a quantitative research design, data were collected from 350 hotel guests in China who had …

Modified Hungarian Method For Lecturer To Course Assignment A Multi Objective Mathematical Programming Model, Nur Syahirah Ibrahim, Adibah Shuib, Zati Aqmar Zaharudin

Malaysian Journal of Computing (MJoC)

Efficient lecturer-to-course assignment is crucial for ensuring both faculty satisfaction and optimal teaching outcomes in higher education institutions. This study presents an advanced optimization model based on the Modified Hungarian Method (MHM) to address this challenge by integrating lecturers' preference levels and competency scores. While previous research has primarily focused on the traditional Hungarian Method (HM), limited attention has been given to its modified version. Moreover, the incorporation of preference-competency-based criteria in lecturer assignments is still lacking. To bridge these gaps, this study develops a mathematical programming approach to refine the MHM framework. The proposed model, called the Preference-Competency Multi-Objective …

Governance In The Absence Of Government, Tracy Hresko Pearl

Faculty Articles

Artificial intelligence (AI) is advancing at an unprecedented pace, with generative systems exerting growing influence over social, economic, and political life. While Al offers opportunities for innovation and efficiency, it also poses risks ranging from misinformation and job displacement to existential threats if highly autonomous systems evade human control. Across industry, government, and civil society, there is broad consensus that Al requires oversight.

Yet traditional U.S. regulatory approaches face six significant barriers: (1) technology outpacing legislation, (2) limited Al expertise among policymakers, (3) regulatory capture, (4) political gridlock, (5) outdated governance structures, and (6) the inherent complexity of Al. Combined …

Robust Hashing For Improved Cnn Performance In Image-Based Malware Detection, Sanket Shekhar Kulkarni, Fabio Di Troia

Faculty Research, Scholarly, and Creative Activity

This paper presents a comparative study on the impact of robust hashing in enhancing image-based malware classification. While Convolutional Neural Networks (CNNs) have shown promise when working with image-based malware samples, their performance degrades significantly when obfuscation techniques are taken into consideration to hamper the malware classification or detection. To address this, we apply a robust hashing technique that generates invariant visual representations of malware samples, enabling improved generalization under obfuscation implemented as image salting. Using a custom obfuscation method to simulate polymorphic variants, we evaluate MobileNet, ResNet, and DenseNet architectures across five salting conditions (0% to 40%). The results …

Machine Learning For Dissolved Oxygen Prediction In The Manawatu River Catchment, New Zealand, Imokhai T. Tenebe, Eunice O. Babatunde, Nkpa M. Ogarekpe, Egbe Etu Etu, Joshua Emakhu, Kindness E. Etu, Obiora B. Ezeudu, Oluwarotimi S. Oladele

Faculty Research, Scholarly, and Creative Activity

Uncontrolled anthropogenic activities have increased river pollution, resulting in poor water quality and a growing need for continuous monitoring, which is time-consuming and costly. This study evaluated the accuracy of machine learning (ML) models in predicting dissolved oxygen (DO) levels, aiming to reduce field monitoring efforts and minimize the number of measured parameters. Regression models (Huber, Linear, Ridge), Bagging methods (Extra Trees, Random Forest, Decision Trees), and Boosting techniques (Gradient Boosting, Light Gradient Boosting, AdaBoost) were tested using data from three sites in the Manawatu Catchment, New Zealand. Data spanning 1989–2014 included 12 water quality variables and was split into …

Finding Aid For University Of Mississippi Small Manuscripts(Mum01771)

Archives & Special Collections: Finding Aids (alphabetical)

The collection contains items related to the University of Mississippi. The collection is arranged by subject.

Safe Sex In The Age Of Big Tech Feminism, Brenda Dvoskin, Thomas E. Kadri

Articles

Lawmakers and technology companies are regulating online sexuality in the name of feminism. Whereas libertarian ideals dominated early debates about internet governance, "safety" has become the rallying cry to regulate online activity in the age of Big Tech. Legislators across the political spectrum and companies around the world are devising interventions meant to keep people - and especially women safe from the risks of online sexuality. Through law and technology, they target everything from privacy invasions to unwanted messages to sexual deepfakes. We call this regulatory and ideological trend Big Tech feminism.

This Article interrogates the feminist strands animating Big …

Law Grapples With Its Achilles’ Heel? Uncertainties, Digital Experiments, And The Ai Frontier, Heng Wang

Research Collection Yong Pung How School Of Law

The governance of digitalization— which encompasses developments such as artificial intelligence (AI) and central bank digital currencies (CBDCs) — confronts serious challenges. At the core of the challenges are uncertainties, which form the central obstacle to effective governance. These uncertainties range from cyber risks to shifting societal responses. Law, as an institutionalized framework of governance, faces mounting pressure and wrestles with a fundamental vulnerability: traditional legal frameworks struggle to address uncertainties in digitalization.

To tackle the pervasive challenges of uncertainties, this article addresses two connected questions: What uncertainties does law face in governing digitalization shaped by emerging technologies? How can …

Integrating Demand Forecasting And Deep Reinforcement Learning For Real-Time Electric Vehicle Charging Price Optimization, Monowar Mahmud, Tarek Abedin, Md Mahfuzur Rahman, Shamiul Ashraf Shoishob, Tiong Sieh Kiong, Mohammad Nur-E-Alam

Research outputs 2022 to 2026

The rapid growth of electric vehicles (EVs) demands efficient, grid-friendly charging systems. This study introduces a dynamic pricing framework combining short-term demand forecasting and deep reinforcement learning. Using Adaptive Charging Network (ACN) data, XGBoost predicts charging demand accurately (R² = 0.84, MAE = 0.45 kW). Compared to a uniform rate applied to all charging usage, set at 0.15 USD/kWh across all hours, with no adjustment for system demand conditions or time-of-day, the optimized strategy enhanced total daily revenue by 133 % and diminished load variance by 72.37 %. The PPO agent also surpassed traditional Time-of-Use and demand-based pricing models …

Design Of A Robust Adaptive Cascade Fractional-Order Proportional–Integral–Derivative Controller Enhanced By Reinforcement Learning Algorithm For Speed Regulation Of Brushless Dc Motor In Electric Vehicles, Seyyed Morteza Ghamari, Mehrdad Ghahramani, Daryoush Habibi, Asma Aziz

Research outputs 2022 to 2026

Brushless DC (BLDC) motors are commonly used in electric vehicles (EVs) because of their efficiency, small size and great torque-speed performance. These motors have a few benefits such as low maintenance, increased reliability and power density. Nevertheless, BLDC motors are highly nonlinear and their dynamics are very complicated, in particular, under changing load and supply conditions. The above features require the design of strong and adaptable control methods that can ensure performance over a broad spectrum of disturbances and uncertainties. In order to overcome these issues, this paper uses a Fractional-Order Proportional-Integral-Derivative (FOPID) controller that offers better control precision, better …

Effects Of Exercise On Inflammation In Female Survivors Of Nonmetastatic Breast Cancer: A Systematic Review And Meta-Analysis, Francesco Bettariga, Dennis R. Taaffe, Anita Borsati, Alice Avancini, Sara Pilotto, Stefano G. Lazzarini, Pedro Lopez, Luca Maestroni, Umberto Crainich, John P. Campbell, Timothy D. Clay, Daniel A. Galvão, Robert U. Newton

Research outputs 2022 to 2026

Background: Despite advances in breast cancer treatment, recurrence remains common and contributes to higher mortality risk. Among the potential mechanisms, inflammation plays a key role in recurrence by promoting tumor progression. Exercise provides a wide array of health benefits and may reduce inflammation, potentially reducing mortality risk. However, the effects of exercise, including mode (ie, resistance training [RT], aerobic training [AT], and combined RT and AT) and program duration, on inflammatory biomarkers in breast cancer survivors remain to be elucidated. Methods: A systematic search was undertaken in PubMed, CINAHL, Embase, SPORTDiscus, and CENTRAL in August 2024. Randomized controlled trials examining …

A Debt To Whom? The Nature Questions In Sino‑Sri Lankan Development Narratives, Orlando Woods, Kanchana Ruwanpura, Loritta Chan, Barnabas Mah

Research Collection College of Integrative Studies

This chapter critically examines dominant narratives around Sri Lanka’s debt and development in the context of Sino‑Sri Lankan relations and the Belt and Road Initiative, questioning assumptions about “debt‑trap” explanations and highlighting how narratives about development, nature and agency shape understanding of the country’s economic and ecological futures.

On Modeling Of Multiplicative Bias Factor For Multivariate Degradation Data, Bingxin Yan, Qiuzhuang Sun, Zhisheng Ye

Research Collection College of Integrative Studies

Multivariate degradation data are increasingly common due to advances in sensor technology. They provide rich information for reliability analysis and remaining useful life (RUL) prediction. The multivariate measurement typically contains actual degradation of multiple performance characteristics (PCs) and measurement errors. The multiple PCs often have different physical meanings with different orders of magnitude, making the associated measurement errors vary in the orders of magnitude as well. It is highly desirable to have a parsimonious model accounting for the possible correlation in degradation measurements, so that more flexibility can be reserved for modeling the actual multivariate degradation. This study proposes a …

Make Yourself Comfortable: Nudging Urban Heat And Noise Mitigation With Smartwatch-Based Just-In-Time Adaptive Interventions (Jitai), Clayton Miller, Xuan Yun Chua, Matias Quintana, Binyu Lei, Filip Biljecki, Mario Frei

Research Collection College of Integrative Studies

Humans can play a more active role in improving their comfort in the built environment if given the right information at the right place and time. This paper outlines the use of Just-in-Time Adaptive Interventions (JITAI) implemented in the context of the built environment to provide information that helps humans minimize the impact of heat and noise on their daily lives. This framework is based on the open-source Cozie iOS smartwatch platform. It includes data collection through micro-surveys and intervention messages triggered by environmental, contextual, and personal history conditions. An eight-month deployment of the method was completed in Singapore with …

Hlcg: A Hierarchical Lane-Changing Gaming Decision Model For Heterogeneous Traffic Flow On Two-Lane Highways, Tianyi Wang, Chong He, Hao Li, Yixuan Li, Yiming Xu, Yangyang Wang, Junfeng Jiao

Research Collection College of Integrative Studies

Discretionary lane-changing behavior is one of the most common highway operations, which seriously affects traffic efficiency and safety. Nowadays, connected and automated vehicles (CAVs) are advancing rapidly, though not yet fully widespread. As a result, a mixed traffic environment with traditional human-driven vehicles (HDVs) and CAVs will persist for the foreseeable future. To achieve effective automatic lane-changing maneuvers, it’s necessary to propose a lane-changing decision model for heterogeneous traffic flow on two-lane highways. This paper firstly extends longitudinal car-following models based on the intelligent driver model and lateral lane-changing models using quintic polynomial curves to accommodate heterogeneous traffic flow, and …

Look Before You Decide: Prompting Active Deduction Of Mllms For Assumptive Reasoning, Yian Li, Wentao Tian, Yang Jiao, Jingjing Chen, Tianwen Qian, Bin Zhu, Na Zhao, Yu‑Gang Jiang

Research Collection School Of Computing and Information Systems

Recently, Multimodal Large Language Models (MLLMs) have achieved significant success across multiple disciplines due to their exceptional instruction-following capabilities and extensive world knowledge. However, whether these MLLMs possess human-like compositional reasoning abilities remains an open problem. To unveil their reasoning behaviors, we first curate a Multimodal Assumptive Reasoning Benchmark (MARS-Bench) in this paper. Interestingly, we find that most prevalent MLLMs can be easily fooled by the introduction of a presupposition into the question, whereas such presuppositions appear naive to human reasoning. Besides, we also propose a simple yet effective method, Active Deduction (AD), a novel reinforcement learning paradigm to encourage …

Digital Regulation And Development: A Global Micro And Macro Comparison, Katrin Kuhlmann

Georgetown Law Faculty Publications and Other Works

Regulation of the digital economy is receiving increased attention both domestically and internationally, but too little scholarship exists assessing the degree to which these new rules effectively support economic, social, and sustainable development. This Article advances a micro-macro framework for assessing digital regulation and its development dimension, including digital inclusion and measures to address the digital divide, the protection of human rights, and the operationalization of the United Nations’ Sustainable Development Goals. In doing so, it incorporates a “micro international law” methodology to compare domestic legal design features and trace their diffusion into regional trade agreements and, in turn, into …

Fairer Public Benefit In Copyright Law, Amanda Levendowski Tepski

Georgetown Law Faculty Publications and Other Works

In 1966, a court considered expressly whether a secondary use of copyrighted works served a public benefit. While public benefit has become a subfactor of the fair use doctrine, it remains undefined, uncodified, and undertheorized. Courts often invoked public benefit in pivotal cases involving novel technologies, from home video recorders to digital libraries to algorithms--but they did not need to. After the recent Supreme Court decision in Google v. Oracle, however, public benefit is unavoidable: The Court stated that “we must take into account the public benefits the copying will likely produce.” A qualitative analysis of key public benefit cases …

Pan-Uk Biobank Genome-Wide Association Analyses Enhance Discovery And Resolution Of Ancestry-Enriched Effects, Konrad J Karczewski, Rahul Gupta, Masahiro Kanai, Wenhan Lu, Kristin Tsuo, Ying Wang, Raymond K Walters, Patrick Turley, Shawneequa Callier, Nirav N Shah, Nikolas Baya, Duncan S Palmer, Jacqueline I Goldstein, Gopal Sarma, Matthew Solomonson, Nathan Cheng, Sam Bryant, Claire Churchhouse, Caroline M Cusick, Timothy Poterba, John Compitello, Daniel King, Wei Zhou, Cotton Seed, Hilary K Finucane, Mark J Daly, Benjamin M Neale, Elizabeth G Atkinson, Alicia R Martin

Faculty, Staff and Students Publications

Large biobanks, such as the UK Biobank (UKB), enable massive phenome by genome-wide association studies that elucidate genetic etiology of complex traits. However, individuals from diverse genetic ancestry groups are often excluded from association analyses due to concerns about population structure introducing false positive associations. Here, we generate mixed model associations and meta-analyses across genetic ancestry groups, inclusive of a larger fraction of the UKB than previous efforts, to produce freely-available summary statistics for 7,266 traits. We build a quality control and analysis framework informed by genetic architecture. Overall, we identify 14,676 significant loci (p < 5 × 10−8) in the meta-analysis that were not found in the EUR genetic ancestry group alone, including novel associations for example between CAMK2D and triglycerides. We also …

Young Feminist Organizing In The State Of Yucatán, Mexico: A Case Study, Natalia Tenorio, Sonia Cruz, Esperanza Basurto, Anel Bobadilla, Paulina Márquez, Ricardo Regules

Adolescents and Young People

As part of the Participatory Action Research (PAR) Study on Young Feminist Organizing in Mexico and Kenya, this case study was conducted in the Yucatán, Mexico to understand the particularities of the feminist movement and organization led by young women in this state. The case study included in-depth interviews and focus groups with representatives of feminist organizations and collectives, civil society organizations, and public institutions. These were conducted in Mérida, the capital of the state of Yucatán, as described in the methodological note.

This brief summarizes the current issues that mobilize young feminists in the state of Yucatán, their forms …

Afit Librarian Newsletter : Check It Out (Fourth Quarter 2025), D'Azzo Research Library, Air Force Institute Of Technology

AFIT Library Knowledge Base

No abstract provided.

Deep Learning-Based Change Detection In High-Resolution Remote Sensing Imagery, Hazem Badawy

Theses and Dissertations

Remote sensing has become a key tool for monitoring Earth’s surface over time, offering valuable insights into both natural and human-driven changes. Among its many applications, change detection focuses on analyzing multi-temporal imagery to reveal how specific areas evolve across different time periods. It plays a pivotal role in Earth observation applications, including urban development monitoring, environmental degradation assessment, and disaster response. However, existing approaches often struggle with limited contextual awareness, high sensitivity to noise, and imprecise localization of change boundaries, especially with high-resolution imagery. This thesis investigates the complex problem of change detection in remote sensing imagery by proposing …