Other Computer Sciences Commons^™

Image-Based Detection And Classification Of Poultry Diseases From Chicken Droppings In Open House Poultry Farms, Md Najmul Hasan, Zu Jun Khow, Vik Tor Goh, Sarina Mansor, Yi-Fei Tan

Journal of Informatics and Web Engineering

Monitoring chicken health is essential for maintaining the production efficiency of poultry farms and meeting the demand for poultry products. Previous studies have explored various methods, including utilizing sound, behaviour, and the shape of the chickens, as well as the conditions of their droppings, to assess chicken health. In this research, we monitor chicken droppings as a reliable indicator of chicken health. We develop an automated system for detecting chicken droppings and identifying health conditions, specifically in open house poultry farms in Malaysia. Open poultry houses are the most common design in Malaysia due to their lower construction and maintenance …

Analysis Of Forensic Disk Imaging Tools For Data Acquisition And Preservation, Michelle Chee Ern Lim, Brandon Chen Hong Chow, Le Ying Lim, Tarini Shanbagamaran, Darren Yong Jun Lim, Ngu War Hlaing, Ahmad Sahban Rafsanjani

Journal of Informatics and Web Engineering

The identification, preservation, analysis, and presentation of electronic evidence to support legal or organizational inquiries constitute the discipline of digital forensics, which is crucial to contemporary investigations. A crucial component of forensic inquiry, disk imaging guarantees precision, dependability, and legal defensibility. To preserve the original evidence, disk imaging makes an identical, bit-by-bit duplicate of a digital storage device, capturing hidden data, deleted material, and active files. Given the critical role of disk imaging in forensic investigations, selecting the right tool is crucial for accuracy, efficiency, and compliance with forensic standards. This study assesses widely used tools, including AccessData FTK Imager, …

Performance Benchmarking: Pre-Trained Models And Custom Convolutional Neural Networks In Deep Learning, Sheemona Joseph C., S. Ganesh, S. Kannadhasan, K. Selvipriya

Journal of Informatics and Web Engineering

Recent advances in computer vision and deep learning, particularly Convolutional Neural Networks (CNNs), have significantly increased road safety. CNNs were used in this work to automatically detect and categorise traffic signs—a crucial task for autonomous vehicles (AVs) and advanced driver assistance systems (ADAS). These technologies' ability to accurately recognize traffic signs enables them to make informed decisions in real time, thereby elevating the standard for overall driving safety. The study uses a large, annotated dataset of images of traffic signs to train and assess the CNN model. We developed a model that can recognize a large number of traffic lights, …

Editorial: Augmented Intelligence For Enabling Knowledge-Driven Decision Making, Heru Agus Santoso

Journal of Informatics and Web Engineering

More flexible and cooperative decision-making processes are required as a result of society's digital transformation. The role of augmented intelligence, that is the synergistic fusion of artificial intelligence and human judgment in facilitating knowledge-driven strategies across various domains is examined in this thematic issue. The integration of business intelligence and software engineering, which forms the foundation for creating intelligent, scalable, and explicable systems, is essential to this investigation. The six chosen papers in this issue show how machine learning techniques can be used to mine and model both structured (such as health records indicators) and unstructured (such as product reviews, …

Exploring The Relationship Between Academic Achievement And Chatgpt Usage: A Survey Of Higher Education Students In Malaysia, Mawar Madiah, A'Tiqah Rashidah Abu Samah

Journal of Informatics and Web Engineering

Students' academic achievement is caused by several factors which include cognitive capability, study habits, instructor effectiveness, family support, socio-economic status, and access to modern learning technologies like ChatGPT. Since its introduction, studies have suggested that ChatGPT has significantly impacted students’ academic performance. This study aims to explore whether students who perform well academically in higher education are more likely to use ChatGPT to enhance their studies and to gather their opinions on this learning tool. A survey was conducted with forty-one students with excellent academic performance. The results of this study show that most students with high academic achievement view …

Performance Evaluation On Covid-19 Prediction Using Machine Learning Models, Obai Ali Abderlahman, Naveen Palanichamy, Su-Cheng Haw, Subhashini Gopal

Journal of Informatics and Web Engineering

The COVID-19 pandemic has placed enormous strain on providing health care services internationally while reinforcing the argument for the need to strengthen forecasting techniques. Existing forecasting methods have drawbacks, especially in determining the long-term consequences of the pandemic and understanding its broad reach across various locations and populations. This project proposes an evaluation of machine learning (ML) models with the aim of improving predictions, particularly the accuracy in long-term forecasting, of subsequent trends of the COVID-19 pandemic. A systematic review highlights previous forecasting attempts as a reference for the approach. This project emphasizes extensive data collection, model formulation and testing …

Machine Learning Model For Assessing Human Well-Being Using Brain Wave Activities, Sellappan Palaniappan, Rajasvaran Logeswaran, Yoke Leng Yong

Journal of Informatics and Web Engineering

This study presents a novel machine learning approach to assess human well-being through the analysis of brain wave activities. We developed a Random Forest classifier to categorize brain wave patterns into three states of well-being: good, normal, and bad. Using synthetic data simulating electroencephalography (EEG) readings, our model achieved an overall accuracy of 96.17%. The feature importance analysis revealed that alpha waves (34%) and beta waves (29%) were the most significant predictors of well-being states, which aligns with existing neuroscientific literature linking alpha activity to relaxation and beta activity to cognitive engagement. The confusion matrix demonstrated the model's particular strength …

A Conceptual Approach To Predicting Seismic Events And Flood Risks Using Convolutional Neural Networks, Mahmoud Rehan, Wan-Noorshahida Mohd-Isa, Noramiza Hashim

Journal of Informatics and Web Engineering

This paper explores the application of convolutional neural networks (CNNs) in predictive modelling for seismic events and flood risks, with a particular focus on forecasting extreme quantile events that exceed historical data limits. Traditional risk assessment methods often struggle to estimate such extremes, highlighting the need for more advanced predictive models capable of handling rare but high-impact events. This research enhances CNN architecture to improve accuracy in high quantile predictions by integrating multi-source spatiotemporal data, addressing a critical research gap. The methodology involves incorporating diverse datasets, including geospatial, meteorological, and historical seismic or flood records, into CNN models to augment …

The Impact Of Deep Learning In Brain Tumour Analysis, Sangeeta Giri, Manivel Kandasamy, Meet Rafaliya

Journal of Informatics and Web Engineering

The need for early and precise identification of abnormalities has made the detection and classification of brain tumours essential components of medical diagnosis. Because brain tumours are naturally complex and can have a wide range of sizes, shapes, and types, conventional diagnostic techniques like MRI interpretation and manual evaluations are difficult and time-consuming. Traditional methods frequently depend on human expertise, which is prone to errors, delays, and variability. Deep learning (DL) developments, on the other hand, have completely changed this field by providing increased automation, efficiency, and precision in tumour detection and classification because they can automatically extract pertinent features …

Exploring User Perceptions Of Security In Mobile Banking: A Study In Malaysia, Kai En Lim, Ying Han Pang, Shih Yin Ooi, Wan Xuan Kow, Tang Xing Cheang, Mao Wei Tan

Journal of Informatics and Web Engineering

Mobile banking has become increasingly popular in Malaysia due to its convenience and efficiency. However, persistent security concerns such as vulnerabilities to cyber attacks, malware and phishing affect user trust and impede adoption. This study aims to explore how users perceive and experience the security features of mobile banking applications. In spite of the implementation of security controls, understanding user perceptions and experiences remains essential, as these are core factors which influence mobile adoption. Thus, this study investigates the user experience of mobile banking security features in Malaysia. Specifically, we focus on the users’ perceptions, satisfaction levels and concerns. We …

Classification Of Smartphone Product Reviews On E-Commerce Using The Recurrent Neural Network (Rnn) Method, Rajibul Anam, Fernanda Tata Pradhana, Imam Abu Yasin, Junta Zeniarja

Journal of Informatics and Web Engineering

Understanding how consumers behave in e-commerce is essential for businesses, especially in today’s digital world where people rely heavily on online shopping platforms. A key part of this understanding comes from sentiment analysis, which looks at customer reviews to find out what buyers really think and feel about products. However, analysing these reviews is not always straightforward. Many people use informal language, slang, or mixed languages, which makes it hard for computers to interpret their opinions accurately. On top of that, there is often an imbalance in the types of data available, particularly in developing countries, where some opinions might …

Cyber Security Threats Of Using Generative Artificial Intelligence In Source Code Management, Sainag Nethala, Sandeep Kampa, Srinivas Reddy Kosna

Journal of Informatics and Web Engineering

Generative Artificial Intelligence (Generative AI) models are now broadly used for academic writing and software development for the sake of productivity and efficiency. Concerns on the impact of Artificial Intelligence (AI) tools on academic integrity and cybersecurity grow bigger with time. Generative AI is being used for code generation, editing, and review, raising ethical and security challenges. A big concern is the involuntary introduction of vulnerabilities into codebases. They can reproduce known bugs or malicious code that compromise software integrity because of the way models are trained: on large datasets. The tools may also pose additional security threats often encountered …

Anomaly Detection In Network Traffic For Insider Threat Identification: A Comparative Study Of Unsupervised And Supervised Machine Learning Approaches, Sellappan Palaniappan, Rajasvaran Logeswaran, Shapla Khanam

Journal of Informatics and Web Engineering

Insider threats pose a significant and growing risk to organizational cybersecurity, with recent studies indicating a 47% increase in insider incidents from 2018 to 2022. This paper presents a comparative analysis of unsupervised and supervised machine learning approaches for detecting potential insider threats through network traffic anomaly identification. We develop and evaluate an Isolation Forest (unsupervised) and a Random Forest (supervised) model, training them on a simulated dataset representing six months of network logs from a mid-sized company. Our study introduces a unique feature set combining traditional network metrics with temporal and behavioral indicators, enhancing the models' detection capabilities. Results …

Dynamic Job Recommendation By Profiling Undergraduates Academic Performances, Bao-Ling Foo, Choo-Yee Ting, Hui-Ngo Goh, Albert Quek, Chin-Leei Cham

Journal of Informatics and Web Engineering

Job-seeking tasks are always challenging. Often, job recommendation systems require human intervention in the job-seeking process. Therefore, the study focuses on recommendation of most relevant job sectors and prioritizing companies based on a student’s profile. The objectives of this study are: (i) to identify important features that optimize job recommendation, (ii) to construct a predictive model that recommends most relevant job sectors, and (iii) to recommend companies by computing the similarity between student and job profiles. In this study, the dataset was collected from Graduate Tracer Study from a university. Additionally, a job dataset was collected to extend the training …

Detecting Black Hole Attack Using Support Vector Machine With Xgboosting In Mobile Ad-Hoc Networks, Anhar Al Madani, Saima Anwar Lashari, Sana Salah Uddin, Abdullah Khan, Muhammad Nouman Atta, Dzati Athiar Ramli

Journal of Informatics and Web Engineering

Mobile Ad-Hoc Networks (MANET) is a type of ad-hoc networks which use less infrastructure, that means the nodes in this network forward the massages without the need of infrastructure such as routers, switches etc. One of the most used attacks that can affect MANET performance is the black hole attack. This attack leads to dropping the packets that means these packets will never arrive and it will decrease the delivery ratio for the packets. This attack is a real problem as the sender is not informed that the data has not reached the intended receiver. The main goal of this …

Generative Ai-Based Meal Recommender System, Zheng Bin Ter, Palanichamy Naveen, J. Jayapradha

Journal of Informatics and Web Engineering

Maintaining a balanced diet is essential for overall well-being, yet many individuals face challenges in meal planning due to time constraints, limited nutritional knowledge, and difficulty aligning meals with personal dietary needs. Traditional meal recommender systems often rely on predefined plans or collaborative filtering techniques, limiting their adaptability and personalization. This study presents a generative AI-based Meal Recommender System utilizing Variational Autoencoders (VAEs) to generate personalized and nutritionally balanced meal plans. The system processes user inputs, such as dietary preferences, nutritional goals, and ingredient availability, to provide tailored recommendations. VAEs effectively uncover hidden dietary patterns and nutritional relationships within complex …

Exploring Big Data Management Approaches And Applications: A Case Study Of Real-Time Data Analytics In Air Traffic Management, Adeel Hashmi, Nouman Amjad, Muhammad Moiz Ullah Satti, Umar Hayat, Anam Mumtaz

Journal of Informatics and Web Engineering

The rapid proliferation of digital devices has generated vast amounts of data, presenting significant challenges in collection, processing, and analysis that traditional systems struggle to overcome. This study investigates big data management approaches, explicitly focusing on technologies capable of efficiently handling real-time data at scale. Within the context of Air Operations, we propose a Hadoop-based architecture designed to support the Observe-Orient-Decide-Act (OODA) loop and enhance air traffic management. By leveraging a distributed system deployed on a cloud-based platform, we demonstrate a cost-effective solution for optimised data processing and improved decision-making capabilities. Our analysis highlights the advantages of using Hadoop's distributed …

The Critical Plastocapillary Number For A Newtonian Liquid Filament Embedded Into A Viscoplastic Fluid, Mohammad Tanver Hossain, Wonsik Eom, Arjun Shah, Andrew Lowe, Douglas Fudge, Sameh H. Tawfick, Randy Ewoldt

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

The yield stress of a viscoplastic material can stabilize an embedded fluid tunnel against capillarity-induced breakup, enabling remarkable technologies such as embedded 3D printing of intricate, freeform, and small components. However, there is persistent disagreement in the published literature between the observed minimum stable diameter, 𝑑min, and the theoretical plastocapillary length 𝑝𝑐 = 2𝛤∕𝜎𝑦, with interfacial tension 𝛤 and bath yield stress 𝜎𝑦, leading to a prior hypothesis that the apparent surface tension 𝛤 is much smaller to enforce 𝑑min = 𝑝𝑐 . Here we introduce and experimentally test a new hypothesis that the critical diameter is set by the …

Neat Vs. Scruffy: How Early Ai Researchers Classified Epistemic Cultures Of Knowledge Representation, Lindsay Poirier

Statistical and Data Sciences: Faculty Publications

Accounts of the history of symbolic artificial intelligence (AI) often categorize researchers and their programs through binary oppositions: logicist versus procedural, or alternatively, neat versus scruffy. These terms were often referenced throughout the 1970s and 1980s by AI researchers at institutions in the United States and Europe to document epistemic and esthetic differences in approaches to knowledge representation. In this article, I historicize and situate these binary oppositions, showing how researchers leveraged them in discourse to demarcate the beliefs and commitments underpinning certain AI approaches. I argue that the labels helped knowledge representation scholars identify, trouble, and grapple with the …

Tellings Of The Pacific Ocean: A Landscape-Based Approach For Multispecies Design And Hci, Maliheh Ghajargar

Engineering Faculty Articles and Research

Environmental disturbances induced by climate change have caused significant changes in our ecosystems and are threatening the health of our environments. As a response to this issue, a growing body of work has emerged in HCI and design, which seeks to foreground more-than-human stories in support of making more sustainable and just futures. This research contributes to this broad agenda by probing graphic novels as a multispecies storytelling method for design and HCI. Combining ideas from Anna Tsing’s adventures of landscape and from HCI and design’s use of sequential art (e.g., storyboards), we use landscape as the main protagonist of …

Video Game Hacking, A General Problem With Generalized Solutions, Luke Rowe

Master's Theses

As video games continue to get more popular and lucrative, the number of malicious actors seeking to exploit them grows with it. As this industry expands, so does the importance of securing games against cheating and abuse. This thesis aims to educate developers to help mitigate the abuse of video games by these malicious actors. The goal of this thesis is to provide a foundational framework for thinking like a hacker and how to make games harder to abuse once a hacker bypasses conventional anti-cheat software.

This thesis outlines some of the most common cheating methods and provides general context …

Optical Character Recognition For Early Handwriting Legibility Assessment, Franceli L. Cibrian, Kayla Anderson, Yingying 'Yuki' Chen, Lauren Min, Lizbeth Escobedo

Engineering Faculty Articles and Research

Monitoring children’s handwriting, such as avoiding writing assignments, displaying uneven letter formation, or showing slow writing speed, can help identify developmental and academic issues early. Poor handwriting affects up to 34% of children, leading to academic and self-esteem challenges. Handwriting assessments, typically conducted by teachers, are often delayed due to workload and could be subjective and inconsistent. This paper explores the potential of Optical Character Recognition (OCR) technology to augment and ease handwriting assessments. Based on an evaluation of 10 OCR algorithms using 33 handwriting samples assessed by two experts, the research indicates that Pen to Print and Google are …

Integrative Computational Modeling Of Distinct Binding Mechanisms For Broadly Neutralizing Antibodies Targeting Sars-Cov-2 Spike Omicron Variants: Balance Of Evolutionary And Dynamic Adaptability In Shaping Molecular Determinants Of Immune Escape, Mohammed Alshahrani, Vedant Parikh, Brandon Foley, Gennady M. Verkhivker

Mathematics, Physics, and Computer Science Faculty Articles and Research

In this study, we conducted a comprehensive analysis of the interactions between the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein and four neutralizing antibodies—S309, S304, CYFN1006, and VIR-7229. Using integrative computational modeling that combined all-atom molecular dynamics (MD) simulations, mutational scanning, and MM-GBSA binding free energy calculations, we elucidated the structural, energetic, and dynamic determinants of antibody binding. Our findings reveal distinct dynamic binding mechanisms and evolutionary adaptation driving the broad neutralization effect of these antibodies. We show that S309 targets conserved residues near the ACE2 interface, leveraging synergistic van der Waals and electrostatic interactions, while S304 focuses on …

Towards Multi-Modal Multi-Document Understanding Capabilities In Foundation Models, Chuhan Li

Computer Science Theses

Contemporary foundation models are predominantly evaluated on isolated documentor image-understanding tasks, thereby overlooking the inherent multimodal multi-document reasoning that characterizes scientific inquiry. To bridge this gap, M3SCIQA is introduced, aMulti-Modal,Multi-document Scientific Question Answering benchmark crafted to test foundation models in practical scientific research settings. A comprehensive evaluation of 18 leading foundation models shows a substantial performance gap between models and human experts. Detailed error analysis reveals persistent deficiencies in both scientific visual reasoning tasks and long-range retrieval. Addressing the former, SPACECUE offers a concise yet effective visual prompting that overlays grid coordinates and Semantic-SAM masks …

Programming A More Efficient Onboarding Process For New Employees, Long H. Pham

Undergraduate Honors Theses

The current onboarding process for new hires in the University of San Diego’s Shiley-Marcos School of Engineering is inefficient. There is no central location where new hires and administrators can track onboarding progress. Both parties have to manage multiple email chains and write their own reminders to keep track of everything. This leads to delays, missing deadlines, and confusion for both parties. A web-based onboarding application has been developed recently to address these issues and streamline the onboarding process for new hires. However, this application contains several accessibility issues and does not follow all of the standards for effective employee …

Hybrid Graph-Recurrent Architecture For Citation Recommendation Via Future Embedding Forecasting, Mohammad Ausaf Ali Haqqani

Computer Science and Engineering Theses and Dissertations

The rapid expansion of scientific literature has intensified the challenge of identifying relevant citations, particularly for newly published or under-cited papers. Traditional citation recommendation systems typically model static relationships or respond to past citation activity, offering limited predictive power for emerging works. In response, this thesis presents a temporal modeling framework for citation recommendation that anticipates future scholarly relevance by forecasting the latent representations of academic papers.

Building on prior work that utilized Temporal Graph Networks (TGNs) to model dynamic citation flows, we propose Graph-Time, a hybrid architecture that integrates a Graph Transformer with a GRU-based time series predictor. The …

Real-World Implementation Of A Noninvasive, Ai-Augmented, Anemia-Screening Smartphone App And Personalization For Hemoglobin Level Self-Monitoring, Robert G. Mannino, Julie Sullivan, Jennifer K. Frediani, Paul George, Jeremy Whitson, James Tumlin, L. Andrew Lyon, Erika A. Tyburski, Wilbur A. Lam

Engineering Faculty Articles and Research

Anemia, characterized by low blood hemoglobin (Hgb) levels, afflicts >2 billion individuals worldwide. Here, we report real-world data generated by a smartphone app that noninvasively screens for anemia using only “fingernail selfies.” App data for anemia screening were obtained from >1.4 million uses across the United States enabling geographic mapping of Hgb levels. Of those, 9,061 users also self-reported complete blood count Hgb levels for comparison, resulting in accuracy and performance that match gold standard laboratory testing and a sensitivity and specificity of 89% and 93%, respectively, when using an anemia cutoff of 12.5 g/dL. Geotagged data enabled construction of …

Magic: The Gathering Deck Testing And Optimization, Ian B. Watson

Honors Theses

The goal of this project is to provide a tool for players of the trading card game Magic: the Gathering to determine whether or not a given deck is optimally built by outputting relevant information regarding its optimality. This is done through a simulator that plays a one-sided game, recording what cards are played, what turn they are played, and how much mana is left over at the end of every turn. The simulator was tested with both optimized and unoptimized decks to show how it can be used to diagnose both.

Optimizing Information Security In Cloud Environments: A Risk Management Approach And Guide For Enterprise Cloud Security, Joshua Olusegun Oyeniyi, Oluwashina Akinloye Oyeniran

Journal of Cybersecurity Education, Research and Practice

In recent years, cloud computing has become increasingly integral to organizational operations due to its scalability, accessibility and cost effectiveness in managing data and resources. However, the rise in security threats and attacks on cloud environments necessitates having robust measures in place to protect data confidentiality, integrity and availability. This paper presents an optimized approach to cloud information security management by reviewing the current threat landscape, evaluating key risk management frameworks, and provided practical solutions for enhancing enterprise cloud security. The study examined three leading cloud security frameworks: the Cloud Controls Matrix (CCM) known for its cloud-specific controls, the NIST …

Designing Ai-Driven Dining: A Ux Approach To Enhancing The Self-Service Experience, Kaylin Joung, Yuki Hayashi, Dailuaine Esguerra

Undergraduate Research Symposium Posters

Artificial intelligence has transformed many industries, yet its integration into self-dining experiences is still emerging. This research explores how AI can enhance self-dining by introducing technology like interactive kiosks and robot servers to improve efficiency, personalization, and customer convenience. By addressing current gaps, we aim to create a more seamless and engaging dining experience.