Databases and Information Systems Commons^™

Machine Learning-Based Prediction Of House Sale Prices In Hulu Langat, Kath Moon Yap Choo, Sew Lai Ng, Da An

Journal of Informatics and Web Engineering

House price prediction remains a complex task due to the interplay of structural, locational, and economic factors. This study proposes a machine learning-based predictive framework tailored to the residential real estate market in Hulu Langat, Malaysia, a district undergoing rapid urbanization. Using open-source datasets, three structured variations are developed: one containing only housing and locational attributes, another incorporating regional-level macroeconomic data, and a third combining both regional and national macroeconomic indicators. The proposed model (Ensemble Weighted Average) is trained and evaluated using evaluation metrics, then compared with models such as Random Forest, XGBoost, LightGBM, and Ensemble Stacking. The proposed model …

Design Of A Cnn–Nlp Based Visual Chatbot For E-Commerce Systems, Sidra Kubra, Salma Jahan Nisha, Nbhan D. Salih, Amalina Ibrahim, Wan Noorshahida Mohd Isa

Journal of Informatics and Web Engineering

E-commerce platforms have limitations to assist interactive and highly personalized experiences using conventional text or voice-based chatbots, particularly in context-aware areas where product discovery and visual identification are necessary. Hence, we propose to integrate an image as a visual-based chatbot. We present a conceptual framework that combines Convolutional Neural Networks (CNN) for image recognition with Natural Language Processing (NLP) for dynamic, context-aware search in a chatbot. The CNN model enables the recognition of products from user-uploaded images, while the NLP component processes and generates appropriate responses to enhance the shopping experience by providing suggestions. This hybrid system will be developed …

Ai-Powered Threat Hunting For Email Phishing Attack Detection Using Natural Language Processing (Nlp), Mohd Azriy Akmalhazim Mohd Nazariee, S Prabha Kumaresan, Alaa Haddad, Mohamed Uvaze Ahamed Ayoobkhan

Journal of Informatics and Web Engineering

Phishing attacks remain as a significant cybersecurity threat, aiming to steal sensitive information by exploiting human vulnerability. Traditional phishing email detection often struggles to keep up with the latest attack strategies developed by the attackers which results in high false positive rates and the limited contextual understanding on the email contents. Therefore, to address these challenges, this research proposes a solution via an AI-powered threat-hunting model integrating Natural Language Processing (NLP) techniques for phishing email detection in English through the development of PhishGuard AI application. The application is developed as a web-based software solution designed to be accessible to both …

Ai-Powered Dynamic Encryption And Decryption Defense Model, Chyanne Wen Qian Lor, Ibrahim Yusof, Anang Hudaya Muhamad Amin

Journal of Informatics and Web Engineering

Static cryptographic systems that rely on fixed algorithms and predetermined keys have proven increasingly ineffective in addressing today’s adaptive and AI-powered cyber threats. This paper proposes an Artificial Intelligence (AI)-enabled dynamic encryption and decryption defence model designed to enhance cybersecurity through real-time threat classification and context-aware cryptographic response. The framework combines the Suricata intrusion detection engine with a Random Forest model developed using the CIC-IDS2017 dataset, allowing it to identify and categorize network anomalies into unified groups, including Denial-of-Service (DoS), Distributed Denial of Service (DDoS), Brute Force, and PortScan. Once threats are identified, the system dynamically selects an appropriate encryption …

Ai-Driven Intrusion Detection System For Network Security, Zhi Lin Sarah Teoh, Wee How Khoh, Hui Yen Yap, Pin Shen Teh

Journal of Informatics and Web Engineering

In the evolving landscape of cybersecurity, Intrusion Detection Systems (IDS) play a vital role in safeguarding computer networks against malicious activity. Traditional signature-based IDS approaches are increasingly ineffective in detecting novel, complex, or zero-day attacks due to their reliance on predefined rules. To overcome these limitations, this study proposes an AI-driven IDS that integrates both classical machine learning and modern deep learning techniques. The framework introduces and compares three models such as Support Vector Machine (SVM), Convolutional Neural Network (CNN), and a hybrid CNN-SVM model. The system is designed to analyze network traffic patterns and classify them as either benign …

Deep Learning Model For Burn Injury Assessment With Enhanced Diagnosis Rate, C. Pabitha, K. Revathi, R. Krishna Priya

Journal of Informatics and Web Engineering

The skin is considered as the largest organ of our body specifically satisfies the environment. As a result, the skin is vulnerable to various injuries, especially burns. Burns are linked to high rates of morbidity and mortality and can be fatal. Effective diagnosis supported by precise evaluation of the burn area and the depth of wound is critical for enabling efficacy in clinical settings. The characteristics of a burn wound on the skin include: the wound is abnormal, the skin is infected, there is discomfort, the skin is tight, and there are raised areas of skin. To properly diagnose burn …

Performance Analysis Of Faster R-Cnn And Yolov8 Model For Mango Fruits Detection, Mohd Haris Lye, Marawan Ashraf Eldeib

Journal of Informatics and Web Engineering

Mango cultivation is a vital agricultural sector in many regions, providing significant economic benefits and contributing to environmental sustainability. Traditional mango detection and harvesting methods are prone to error and are labour-intensive. Recent advances in aerial imagery and Artificial Intelligence (AI) offer innovative solutions to these challenges. An automated yield estimation and fruit harvesting system will require an accurate fruit object detection model. This study explores the application of deep learning models for mango detection on two diverse datasets. This research focuses on evaluating two primary deep learning models: Faster Region-based Convolutional Neural Network (Faster R-CNN) and You Only Look …

A Review Of Cyber-Attacks On Web Applications And Their Countermeasures, Salaheddin Beskri, Kok Why Ng

Journal of Informatics and Web Engineering

Leaders have always wondered where and how precisely Information Technology (IT) can be of value in their organizations. However, the pacing rate of technology development and cutting-edge features that technologies can provide can cause the decision-maker of an organization to rush to invest in technology upgrades without taking risks into account. The risks that may cost the existence of organizations can be caused by an adversary who commits a cyberattack against the IT assets of an organization, seizing leaders' underestimations of the risks of IT upgrades. Inevitably, there will always be new vulnerabilities in IT assets, especially in information systems. …

Multi-Agent Ai Systems For Detecting Emerging Therapeutic Targets And Intervention Patterns In Neuroplasticity Research, Raza Hasan, Salman Mahmood

Journal of Informatics and Web Engineering

The exponential growth of neuroplasticity research presents profound challenges to the manual synthesis of literature, hindering the identification of emerging therapeutic targets and intervention patterns. While multi-agent Artificial Intelligence (AI) systems have been proposed conceptually to address this, concrete implementations demonstrating end-to-end utility are scarce. This paper details the successful implementation and application of a novel four-agent AI system designed to automate the discovery, extraction, analysis, and validation of patterns in neuroplasticity literature. Our platform consists of four parts: a Literature Discovery Agent (LDA) for corpus gathering, a Concept Extraction Agent (CEA) with a multi-level NLP strategy fallback mechanism for …

Synthetic Data Generation For Healthcare And Wellness: Methods, Applications, And Future Directions, Sellappan Palaniappan, Kasthuri Subaramaniam, Oras Baker, Bui Ngoc Dung, Sumit Dhariwal

Journal of Informatics and Web Engineering

Artificial intelligence in healthcare relies heavily upon the availability of high-quality datasets, but very rigid privacy regimes, institutional silos, ethical issues, and heterogeneous data still serve to limit the availability of real-world clinical data. To combat these limitations, you focus on synthetic data generation as a privacy-preserving and scalable alternative to traditional data generation for healthcare and wellness studies. More relevant is a work on a broad and clear framework of synthesis methods based on statistical modelling, rule-based generation and domain-specific clinical logic, in contrast to recent studies that mainly target sophisticated methods for the deep learning architectures. The paper …

Ai-Powered Continuous Health Monitoring For Chronic Disease Management In Malaysia: Real-Time Risk Prediction Using Wearable Sensors And Digital Biomarkers, Umi Najiah Ahmad Razimi, Yong Yoke Leng, Nor Hapiza Mohd Ariffin, Mohammed Hazim Alkawaz

Journal of Informatics and Web Engineering

Chronic Non-Communicable Diseases (NCDs), such as diabetes mellitus, hypertension, and cardiovascular disease, make up a significant portion of the mortality cases in Malaysia, as well as impose consistent demands for costs related to health care expenditure. According to the cost reports at the national level, major NCDs (cardiovascular disease, diabetes, and cancer) are responsible for RM 9.65 billion annually as direct healthcare cost [3]. However, the current approach to managing chronic diseases focuses heavily on periodic assessments in clinic setting at weekly or monthly intervals. Here, a predictive analysis framework based on AI technology, leveraging continuous monitoring with patient data …

Intelligent Telemedicine Systems For Contactless Heart Rate Estimation Using Deep Learning-Based Rppg, Do Duc Thinh, Nguyen Duc Manh, Nguyen Thi Bich Ngoc, Vu Van Huan

Journal of Informatics and Web Engineering

Over the last ten years, telemedicine has undergone significant developments, from simple communication-based healthcare to smart and data-driven solutions. The solutions are now powered by Artificial Intelligence (AI), Machine Learning (ML), and Internet of Medical Things (IoMT) technologies. Physiological monitoring is traditionally done with contact-based sensors. The sensors include Electrocardiogram (ECG) and pulse oximeters. The sensors have various disadvantages, including hardware costs, difficulty in continuous usage, and discomfort for patients. Photoplethysmography (rPPG), a remote method of physiological monitoring, is a breakthrough technology. The method is used to estimate physiological signals, including Heart Rate (HR), from video streams captured by standard …

Holistic Strategies For Optimizing Municipal It Operations And Cost Efficiency, Joseph Yazdanpanahi

Certified Public Manager® Applied Research

Municipalities work to deliver secure and impactful IT (information technology) services while often working within tight budget constraints. To address this problem, this article offers an experience-based and comprehensive framework to optimize municipal IT operations. It emphasizes leveraging automation, streamlining hardware and software management, and forging strategic partnerships to drive cost savings and efficiency gains. Designed to equip municipal IT leaders with practical tools and insights, this article aims to help modernize IT processes.

Ms110 Syllabus: Introduction To Computers, Information Systems, And Artificial Intelligence, Wei Zhang

Management Science and Information Systems Faculty Publication Series

This is a syllabus for Professor Wei Zhang's MS110: Introduction to Computers, Information Systems and Artificial Intelligence Course within UMass Boston's College of Management. This is an Open Educational Resource and can be remixed, copied, redistributed, altered and reused as long as permission is given to the original creator.

Shared Language For Responsible Ai Integration, Asa B. Stone, Mark C. Stone, Alisha Bevins, Jean Claude Niyomugabo, Irene Magara, Jacob Abaare, Derek M. Heeren, Mubarak Abu Zouriq

PRAIRIE: Pioneering Responsible AI for Research, Innovation, and Education

As AI rapidly reshapes how we work and learn, employers increasingly seek graduates who can think before they prompt, exercising judgment under pressure rather than merely producing output. Yet students are praised for AI use in one course and penalized for it in the next, and faculty are left to lead responsibly on shifting ground, with no shared language to guide them.

This paper introduces the PRAIRIE Framework for AI Integration, a shift from reactive gatekeeping toward proactive stewardship. It emerged from a qualitative sentiment analysis of three communities (students, faculty, and industry partners) whose concerns converged on one need: …

Vehicle-Based Multi-Services For Future Smart Cities, Hao Sun, Jinhua Zhao, Hai Yang, Shenhao Wang, Hamsa Balakrishnan, Thomas W. Malone, Hai Wang

Research Collection School Of Computing and Information Systems

Vehicles are crucial for sustaining socioeconomic activity and improving quality of life in modern cities by offering diverse services. These include passenger mobility, goods delivery, information acquisition, and acting as mobile servers such as food trucks and mobile lockers. At the same time, they also contribute to traffic congestion and air pollution. This tension fosters the rise of urban resource-conserving and sustainable service solutions. In this article, we introduce the concept of “Vehicle-Based Multi-Services” (VeMuS), in which a single vehicle offers multiple services simultaneously. Drawing on practical use cases, we examine service classification and integration for vehicles and the potential …

Esg Reporting Lifecycle Management With Large Language Models And Ai Agents, Thong Hoang, Mykhailo Klymenko, Xiwei Xu, Shidong Pan, Yi Ding, Xushuo Tang, Zhengyi Yang, Jieke Shi, David Lo

Research Collection School Of Computing and Information Systems

Environmental, Social, and Governance (ESG) standards have been increasingly adopted by organizations to demonstrate accountability towards ethical, social, and sustainability goals. However, generating ESG reports that align with these standards remains challenging due to unstructured data formats, inconsistent terminology, and complex requirements. Existing ESG lifecycles provide guidance for structuring ESG reports but lack the automation, adaptability, and continuous feedback mechanisms needed to address these challenges. To bridge this gap, we introduce an agentic ESG lifecycle framework that systematically integrates the ESG stages of identification, measurement, reporting, engagement, and improvement. In this framework, multiple AI agents extract ESG information, verify ESG …

The Stars Align: Modeling User Rating Calibration With Sparse Semantic Review Features, Rodrigo Alves, Antoine Ledent

Research Collection School Of Computing and Information Systems

User ratings are often treated as comparable across users, although identical scores may reflect different experiences. We study whether ratings can be viewed as user-specific discretizations of a shared semantic continuum derived from review text. Our method maps reviews into sparse semantic features with a sparse autoencoder and learns user-specific filters for each rating level. On Amazon Electronics, the learned embeddings align along a shared low-dimensional rating axis. Users differ mainly in how they anchor and partition this continuum, while preserving its overall ordinal structure. These findings support a semantic view of calibration beyond scalar bias correction.

Trace: Temporal Rhetorical Analysis And Consistency Evaluation For Legislative Speech, David Hernandez

Master's Theses

Legislators frequently discuss the same policy issues across multiple hearings and legislative sessions, sometimes maintaining consistent positions and other times modifying or reframing their stance over time. Understanding how these positions evolve is important for analyzing political discourse and democratic accountability, yet identifying such shifts at scale remains difficult.

We introduce TRACE (Temporal Rhetorical Analysis and Consistency Evaluation), a system built on the Digital Democracy Database (DDDB) for detecting rhetorical inconsistency in California legislative hearing testimony. TRACE organizes utterances into speaker-anchored timelines indexed by bill and session, then applies hybrid semantic retrieval — combining dense BGE embeddings with BM25 lexical …

Political Inconsistency Detection Across Legislative Speech And Public Communications, Scott M. Pramuk

Master's Theses

Political actors communicate about legislation across multiple contexts, including committee hearings, recorded votes, and public-facing press releases. Differences between these forms of communication can provide useful signals for journalists and researchers seeking to understand how legislators present policy positions to different audiences.

This thesis extends the Digital Democracy Project, a legislative transparency initiative that provides access to California state legislative hearing transcripts, voting records, and related legislative data. Specifically, this work incorporates publicly accessible, legislator-authored news releases into the Digital Democracy Database and develops a pipeline for analyzing legislative communication across multiple sources. The system collects news releases from California …