Databases and Information Systems 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 …

A Decision Support System For Conference Session Selection Using Natural Language Processing, Tillman E. Erb

Master's Theses

Conference attendees are faced with selecting from hundreds to thousands of presentations and sessions in pursuit of new findings and methods relevant to their area of interest, an overwhelming amount of information from which to clearly make a decision. To address this, we developed a decision support system leveraging natural language processing (NLP) techniques such as semantic matching. By creating and matching embeddings of conference presentation abstracts and titles, the application provides improved query matching compared to keyword searching. We introduce Session Scout, a novel conference decision support system built upon a semantic retrieval framework. Session Scout is designed to …

Next-Generation Is Research Methods - Towards A Better Understanding Of Complex And Dynamic Phenomena … And Generative Ai As The Elephant In The Room, Ivo Blohm, Shaila Miranda, Shuk Ying Ho, Jan Marco Leimeister

Information Systems Faculty Publications and Presentations

Emerging phenomena like the increasing volume and variety of available data, machine learning techniques, and, most recently, Generative AI are reshaping our research practices, affording new research methods for testing and developing theory. In this editorial, we discuss our two major observations from running the 'Next-generation IS Research Methods' Special Issue: (1) the need for research methods that enhance our understanding of complex and dynamic phenomena and (2) Generative AI as (potential) productivity enhancer. We compile these observations into an organizing framework and discuss possibilities for applying Generative AI in the fields of qualitative, quantitative, and engaged research. We highlight …

Rich Models And Methods For On-Demand Same Day Deliveries, Zhiqin Zhang

Dissertations and Theses Collection (Open Access)

Same-day delivery has brought numerous conveniences to people’s lives, but it has also presented challenges in terms of service management. To effectively optimize on-demand same-day delivery operations within urban logistics, intelligent decision-making strategies capable of adapting to rapidly changing circumstances are essential. Employing effective decisionmaking strategies that account for order allocation, route planning, courier scheduling, and other relevant factors, is pivotal in advancing logistics operations, enhancing efficiency, customer satisfaction, and resource utilization in the context of dynamic same-day delivery problems.

The focus of this thesis revolves around different emerging challenges presented by on-demand same-day delivery problems, with a particular emphasis …

Large Language Models For Logical Fallacy Detection, Nicole Anne Hui-Ying Teo, Donghao Huang, Erik Cambria, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Identifying logical fallacies is essential for maintaining log-ical reasoning and reducing false information in a variety of domains, such as the media, law, and education. We present an extensive study on the use of large language models (LLMs) for logical fallacy detection and provide a comparative overview of model performance across various fallacy classes. We evaluate the logical fallacy detection capabilities of multiple state-of-the-art models (LLaMA, Qwen, Gemma, Phi) utilizing accuracy, precision, recall, and F1-score as assessment measures. Accord-ing to our findings, our models do well on simple fallacies like “circular reasoning,” but they have trouble with more interpretive reasoning …

Alayadb: The Data Foundation For Efficient And Effective Long-Context Llm Inference, Yangshen Deng, Zhengxin You, Long Xiang, Qilong Li, Peiqi Yuan, Zhaoyang Hong, Yitao Zheng, Wanting Li, Runzhong Li, Haotian Liu, Kyriakos Mouratidis, Man Lung Yiu, Huan Li, Qiaomu Shen, Rui Mao, Bo Tang

Research Collection School Of Computing and Information Systems

AlayaDB is a cutting-edge vector database system natively architected for efficient and effective long-context inference for Large Language Models (LLMs) at AlayaDB AI. Specifically, it decouples the KV cache and attention computation from the LLM inference systems, and encapsulates them into a novel vector database system. For the Model as a Service providers (MaaS), AlayaDB consumes fewer hardware resources and offers higher generation quality for various workloads with different kinds of Service Level Objectives (SLOs), when compared with the existing alternative solutions (e.g., KV cache disaggregation, retrieval-based sparse attention). The crux of AlayaDB is that it abstracts the attention computation …

Lessons Learned From Sandboxing, Piloting And Policy Experimentation With Ai And Other Digital Initiatives: Part 1, Summary Report, Steven M. Miller

Research Collection School Of Computing and Information Systems

This report, "Lessons Learned from Sandboxing, Piloting and Policy Experimentation with AI and Other Digital Initiatives," captures insights and experiences from project experts involved in recent digital innovation initiatives with the governments of Bangladesh, Maldives, and Kazakhstan, and from project experts actively involved with the use of AI for delivering government digital services in the EU, New Zealand, Rwanda, Singapore, United States, and Uzbekistan. The ten in-depth interview write-ups produced from these nine different country settings provide a small but highly informative sample of rich descriptions of some of the important realities, approaches, nuances, issues and challenges related to testing …

Lessons Learned From Sandboxing, Piloting And Policy Experimentation With Ai And Other Digital Initiatives: Part 2, Ten In-Depth Interviews, Steven M. Miller

Research Collection School Of Computing and Information Systems

This report, "Lessons Learned from Sandboxing, Piloting and Policy Experimentation with AI and Other Digital Initiatives," captures insights and experiences from project experts involved in recent digital innovation initiatives with the governments of Bangladesh, Maldives, and Kazakhstan, and from project experts actively involved with the use of AI for delivering government digital services in the EU, New Zealand, Rwanda, Singapore, United States, and Uzbekistan. The ten in-depth interview write-ups produced from these nine different country settings provide a small but highly informative sample of rich descriptions of some of the important realities, approaches, nuances, issues and challenges related to testing …

Hd-Epic: A Highly-Detailed Egocentric Video Dataset, Toby Perrett, Ahmad Darkhalil, Saptarshi Sinha, Omar Emara, Sam Pollard, Kranti Kumar Parida, Kaiting Liu, Prajwal Gatti, Siddhant Bansal, Kevin Flanagan, Jacob Chalk, Zhifan Zhu, Rhodri Guerrier, Fahd Abdelazim, Bin Zhu, Davide Moltisanti, Michael Wray, Hazel Doughty, Dima Damen

Research Collection School Of Computing and Information Systems

We present a validation dataset of newly-collected kitchenbased egocentric videos, manually annotated with highly detailed and interconnected ground-truth labels covering: recipe steps, fine-grained actions, ingredients with nutritional values, moving objects, and audio annotations. Importantly, all annotations are grounded in 3D through digital twinning of the scene, fixtures, object locations, and primed with gaze. Footage is collected from unscripted recordings in diverse home environments, making HDEPIC the first dataset collected in-the-wild but with detailed annotations matching those in controlled lab environments. We show the potential of our highly-detailed annotations through a challenging VQA benchmark of 26K questions assessing the capability to …

Predicting Consumers’ Purchase Intention Of Browsed Products: A Study Based On Eye‑Tracking, Feiyan Jia, Choon Ling Sia, Yani Shi, Fiona Fui-Hoon Nah, Keng Siau

Research Collection School Of Computing and Information Systems

Predicting consumers’ purchase intention of browsed products enables sellers to implement nuanced promotion strategies to stimulate purchase. But how can we predict consumers’ purchase intention of browsed products? Our research demonstrates that consumers’ eye movement data collected when they browse products can serve this aim. We train and test the prediction model using logistic regression and random forest algorithms. Using data collected in a laboratory experiment, our empirical results show that both algorithms perform much better than a random guess, and the logistic regression performs slightly better than the random forest. Our findings imply that eye movement data enable sellers …

Human-Computer Interaction And Artificial Intelligence For Ageing Population, Keng Siau, Hailiang Wang, Fiona Fui-Hoon Nah, Runyu Wang, Ruitong Che, Can Liu

Research Collection School Of Computing and Information Systems

As the global population ages rapidly, the field of human-computer interaction (HCI) is in urgent need of innovation, redesign, and reengineering to meet the evolving needs of older adults. The older demographic faces a range of challenges—including physical limitations, cognitive decline, reduced social in-tegration, and varying levels of technological literacy—that can hinder effective engagement with digital technologies. In response to these challenges, research-ers and designers are using inclusive and adaptive approaches to enhance acces-sibility, usability, and emotional well-being. This paper reviews key design prin-ciples in HCI for the ageing population and discusses how artificial intelligence (AI) tools, such as voice …

Unlocking The Power Of Socio-Knowledge Association For Enterprise Risk Identification In Stock Market, Zhenghao Liu, Keng Siau, Shaochen Yang, Feicheng Ma

Research Collection School Of Computing and Information Systems

Potential risk signals reflected in supply chain and equity connections between enterprises and social connections between investors are becoming crucial to identifying enterprise risks in addition to basic financial indicators. Traditional risk management systems face challenges in adapting to these complexities, highlighting the need for a proactive paradigm shift in risk management. Leveraging graph models such as social networks and knowledge graphs offers a promising approach to identifying and managing potential associated risks effectively. To bridge existing research gaps, a novel risk identification framework driven by social-knowledge graphs has been proposed, integrating graph deep learning and reinforcement learning techniques guided …

Less Is More: On The Importance Of Data Quality For Unit Test Generation, Junwei Zhang, Xing Hu, Shan Gao, Xin Xia, David Lo, Shanping Li

Research Collection School Of Computing and Information Systems

Unit testing is crucial for software development and maintenance. Effective unit testing ensures and improves software quality, but writing unit tests is time-consuming and labor-intensive. Recent studies have proposed deep learning (DL) techniques or large language models (LLMs) to automate unit test generation. These models are usually trained or fine-tuned on large-scale datasets. Despite growing awareness of the importance of data quality, there has been limited research on the quality of datasets used for test generation. To bridge this gap, we systematically examine the impact of noise on the performance of learning-based test generation models. We first apply the open …

Adapting Large Language Models For Parameter-Efficient Log Anomaly Detection, Ying Fu Lim, Jiawen Zhu, Guansong Pang

Research Collection School Of Computing and Information Systems

Log Anomaly Detection (LAD) seeks to identify atypical patterns in log data that are crucial to assessing the security and condition of systems. Although Large Language Models (LLMs) have shown tremendous success in various fields, the use of LLMs in enabling the detection of log anomalies is largely unexplored. This work aims to fill this gap. Due to the prohibitive costs involved in fully fine-tuning LLMs,we explore the use of parameter-efficient fine-tuning techniques (PEFTs) for adapting LLMs to LAD.To have an in-depth exploration of the potential of LLM-driven LAD, we present a comprehensive investigation of leveraging two of the most …