Other Computer Sciences Commons^™

Connecting Worlds: Travelmate’S Bidding System For Personalized Travel Experiences, Suman Khadka

Williams Honors College, Honors Research Projects

TravelMate is a web platform that connects travelers with local guides through a personalized trip posting system along with real time bidding from freelancer guides. This platform utilizes Next.js for a responsive frontend, and Supabase for managing database and backend RESTful API, using PostgREST - a thin API layer on top of Postgres. This platform is ideal for anyone looking to explore new places with a freelancer guide and gain cultural insights from a local expert who understands the place deeply.

Utilizing Biometrics And Blockchain For Enhanced Security Of Remote Patient Monitoring (Rpm) Data Sharing, Amaturrahman Raihanah Medlock

Dissertations, Master's Theses and Master's Reports

Advancements in Artificial Intelligence (AI) and Internet of Medical Things (IoMT) technologies have significantly revolutionized the conventional healthcare systems. Through the integration of smart devices, medical sensors, and communication technology, IoMT provides real-time patient’s monitoring data for healthcare providers, thus promoting accurate and timely clinical decisions for patient-centric care. The current healthcare sector is evolving to a connected ecosystem with connectivity and intelligence. While it also incurs increasing security and privacy concerns as integrating IoMT generated patient monitoring data into healthcare information systems. Both blockchain and biometrics are measures that have established reputable names in the security realm. When evaluating …

Tintin: A Unified Hardware Performance Profiling Infrastructure To Uncover And Manage Uncertainty, Ao Li, Marion Sudvarg, Zihan Li, Sanjoy Baruah, Chris Gill, Ning Zhang

Computer Science Faculty Research & Creative Works

Hardware performance counters (HPCs) enable the measurement of microarchitectural events, which are crucial for tracking and predicting program behavior. High-fidelity measurement and precise attribution are essential for accurate profiling. However, existing profiling tools have fundamental challenges in both aspects. In measurement, numerous events compete for limited hardware monitoring resources; while for attribution, applications have diverse requirements, but systems provide limited support. Existing tools mitigate the former limitation through event multiplexing, but this approach introduces non-trivial errors. The latter limitation, however, remains largely unaddressed. This paper introduces Tintin, an HPC profiling infrastructure with a modular three-component design that addresses both challenges. …

Exploring Secure Methods For Ensuring Data Integrity: A Theoretical Analysis Of Cryptographic And Detection Techniques, Haryam Garcia Martinez

Electronic Theses, Projects, and Dissertations

This study investigates cryptographic methods to ensure data integrity within cloud environments, with a particular focus on comparing the security, performance, and efficiency of MD5 and SHA-256 hash algorithms. Data integrity is critical for protecting sensitive information, especially in sectors like healthcare, where cloud storage solutions are increasingly prevalent. Through a theoretical analysis, the study evaluates the advantages and limitations of MD5 and SHA-256, emphasizing SHA-256’s stronger security capabilities in preventing collision attacks compared to MD5, albeit with higher resource consumption.

The literature review draws from recent advancements in cryptography, blockchain, and artificial intelligence (AI) technologies, presenting a comprehensive view …

Comparative Analysis Of Linear And Nonlinear Semg Methods For Detecting Muscle Fatigue During Dynamic Biceps Curls, Tang Ming, Ling Weay Ang, Sellappan Palaniappan

Journal of Informatics and Web Engineering

Muscle fatigue, a key concern in sports science, rehabilitation, and occupational health, influences performance, injury risk, and provides insights into muscle functionality and endurance. Surface electromyography (sEMG) has emerged as a vital tool for non-invasively tracking muscle electrical activity and gauging health. As its application for muscle fatigue assessment grows, identifying the most accurate analytical methods is essential. Current sEMG analyses employ both linear and nonlinear metrics to measure fatigue onset and progression, yet research is ongoing to determine which method is most effective in the context of dynamic contractions. The study was aimed to evaluate the efficacy of established …

Enhancing Cybersecurity Awareness Through Gamification: Design An Interactive Cybersecurity Learning Platform For Multimedia University Students, Adlil Khaliq Abdul Razack, Mohamad Firdaus Mat Saad

Journal of Informatics and Web Engineering

Cybersecurity has emerged as a critical imperative in contemporary digital landscapes, necessitating heightened awareness and proficiency across all demographic segments. Accordingly, this research has been meticulously crafted to delve into the complexities of cybersecurity awareness, with a specific focus on university students. The study embarks on an exhaustive analysis encompassing the evaluation of cybersecurity awareness levels within the targeted groups, the identification of prevailing issues and practices, and an exploration of novel methodologies, notably gamification, to fortify cybersecurity knowledge and skills among diverse user cohorts. Central to this investigation is the efficacy of gamified learning environments tailored expressly for augmenting …

Towards Analysable Chaos-Based Cryptosystems: Constructing Difference Distribution Tables For Chaotic Maps, Je Sen Teh, Abubakar Abba

Journal of Informatics and Web Engineering

Chaos-based cryptography has yet to achieve practical, real-world applications despite extensive research. A major challenge is the difficulty in analysing the security of these cryptosystems, which often appear ad hoc in design. Unlike conventional cryptography, evaluating the security margins of chaos-based encryption against attacks such as differential cryptanalysis is complex. This paper introduces a straightforward approach of using chaotic maps in cryptographic algorithms in a way that facilitates cryptanalysis. We demonstrate how a chaos-based substitution function can be constructed using fixed-point representation, enabling the application of conventional cryptanalysis tools such as the difference distribution table. As a proof-of-concept, we apply …

Development Of Virtual Reality-Based Left Brain System, Kok An Teo, Kok Swee Sim, Chean Khim Toa, Kai Liang Lew

Journal of Informatics and Web Engineering

This paper proposes a virtual reality based Left Brain System named BrainUp World to improve left brain thinking. The left-brain system was employed with mobile virtual reality technology, hand motion tracking and haptic feedback system. The implementation of these systems is to enhance the experience and sense of embodiment. BrainUp World includes virtual reality-based brain training games to improve a person’s attention level, reasoning skill, auditory cognition, arithmetic skill, sequencing skill and memory. The hand tracking system was utilized with an IR camera to capture the hand orientation and return the gesture data to the VR application. A low-cost and …

Pi Class – A Revolutionary Step Forward In Hybrid Class Management System, Qasim Hassan, Nabiha Faisal, Ayesha Anees Zaveri

Journal of Informatics and Web Engineering

The COVID-19 pandemic has caused widespread disruptions globally, resulting in a state of health emergency with numerous deaths reported and an overall implementation of quarantine and isolation. Strict lockdowns have been implemented to curb the spread of the virus, requiring social distancing and limiting physical interactions. These measures had far-reaching impacts on all aspects of life, including education. Education was one of the most affected sectors, with difficulty delivering quality education in schools, colleges, and universities. It was hard to provide quality education, a basic need of humanity. In this research paper, we propose an adequate solution to overcome the …

Development Of Robot Feature For Stunting Analysis Using Long-Short Term Memory (Lstm) Algorithm, Muhammad Rahadian Abdurrahman, Halim Al Aziz, Farras Adhani Zayn, Muhammad Agus Purnomo, Heru Agus Santoso

Journal of Informatics and Web Engineering

Stunting prevalence in Indonesia persists as a significant challenge necessitating concerted efforts from all stakeholders. We developed robot for stunting analysis using deep learning algorithm. It aligns with the Sustainable Development Goal (SDG) agenda, specifically targeting SDG 3, which focuses on ensuring good health and well-being for all. Long Short-Term Memory (LSTM) is a type of Recurrent Neural Network (RNN) designed to overcome the vanishing gradient problem in traditional RNNs. In general, either LSTM can be used in analysis. This study aims to classify stunting based on age and height using LSTM. The LSTM model was trained with 50 epochs …

Cyber-Securing Medical Devices Using Machine Learning: A Case Study Of Pacemaker, Suliat Toyosi Jimoh, Shaymaa S Al-Juboori

Journal of Informatics and Web Engineering

This study aims to enhance the cybersecurity framework of pacemaker devices by identifying vulnerabilities and recommending effective strategies. The objectives are to pinpoint cybersecurity weaknesses, utilize machine learning to predict security breaches, and propose countermeasures based on analytical trends. The literature review highlights the transformation of pacemaker technology from basic, fixed-rate devices to sophisticated systems with wireless capabilities, which, while improving patient care, also introduce significant cybersecurity risks. These risks include unauthorized entry, data breaches, and life-threatening device malfunctions. The methodology in this study utilizes a quantitative research approach using the WUSTL-EHMS-2020 dataset, which includes network traffic features, patients' biometric …

Review On Automated Storage And Retrieval System For Warehouse, Alex Low Kai Jie, Sim Kok Swee, Lew Kai Liang

Journal of Informatics and Web Engineering

The swift expansion of e-commerce and supply chain operations has significantly enhanced the efficiency of warehouse management systems, establishing them as vital components in augmenting organizations' competitiveness. This paper delves into warehouse sorting systems to enhance the sorting process, reduce error rates, and simplify internal warehouse procedures. It aims to develop a scalable, adaptable, and efficient warehouse sorting system that can maximize sorting efficiency while effectively responding to changing market demands through the use of advanced automation technologies. The study provides an in-depth review of the literature that has explored the Automated Storage and Retrieval System (ASRS) within the context …

Sibling Discrimination Using Linear Fusion On Deep Learning Face Recognition Models, Rita Goel, Maida Alamgir, Haroon Wahab, Maria Alamgir, Irfan Mehmood, Hassan Ugail, Amit Sinha

Journal of Informatics and Web Engineering

Facial recognition technology has revolutionised human identification, providing a non-invasive alternative to traditional biometric methods like signatures and voice recognition. The integration of deep learning has significantly enhanced the accuracy and adaptability of these systems, now widely used in criminal identification, access control, and security. Initial research focused on recognising full-frontal facial features, but recent advancements have tackled the challenge of identifying partially visible faces, a scenario that often reduces recognition accuracy. This study aims to identify siblings based on facial features, particularly in cases where only partial features like eyes, nose, or mouth are visible. Utilising advanced deep learning …

In Search Of Suitable Methods For Cost-Benefit Analysis Of Cyber Risk Mitigation In Offshore Wind: A Survey, Yvonne Hwei-Syn Kam, Kevin Jones, Robert Rawlinson-Smith, Kimberly Tam

Journal of Informatics and Web Engineering

In recent years, notable incidents have highlighted the vulnerability of wind energy infrastructure, making cybersecurity crucial for the offshore wind industry. However, justifying the costs of cybersecurity measures is essential. A cost benefit analysis (CBA) is commonly utilised to support decision-making for risk mitigation. With a cost benefit analysis, risk mitigation strategies that strike an optimal balance between the costs of mitigation measures and the resulting risk reduction can be identified. This survey of literature was carried out to identify the existing proposed solutions for cost benefit analysis on cyber risk mitigation measures for offshore wind cyber physical systems. After …

Editorial Preview, Su-Cheng Haw

Journal of Informatics and Web Engineering

Effective from Volume 3, JIWE has transitioned to a triannual publication release effort. Specifically, releases occur each February, June, and October. This particular October 2024 release contains 12 papers within the regular section that covers a broad range of application concerning Machine Learning (ML), Artificial Intelligence (AI), Data Mining (DM), Software Engineering, Recommender Systems, Cybersecurity, Healthcare, and other key areas in web engineering. Additionally, this edition presents a captivating collection of 7 papers curated by our Thematic Editor, Dr. Ji-Jian Chin, under the theme "Pervasive Computing." In his editorial, Dr. Ji-Jian Chin highlights cutting-edge research on the integration of computing …

Mobitest – A Software For Mobile-Based Testing, Ayesha Anees Zaveri, Ramsha Mashood, Nabiha Faisal, Misbah Parveen, Naveera Sami, Mobeen Nazar, Saba Imtiaz

Journal of Informatics and Web Engineering

MobiTest is an application that serves as a valuable tool in the fast-growing field of software testing. Efficiency is crucial in this industry, where testers, quality assurance teams, and others must meticulously test each application, avoiding the need to repeat the entire cycle to identify bugs. This application is a breeze thanks to its intuitive features and educational content. Thanks to continuous integration, testers can easily keep up with the fast-paced development cycle and start automating tasks as soon as the user interface development is completed. This saves valuable time and ensures a smoother and more efficient process. During the …

Hybrideval: An Improved Novel Hybrid Metric For Evaluation Of Text Summarization, Raheem Sarwar, Bilal Ahmad, Pin Shen Teh, Suppawong Tuarob, Tipajin Thaipisutikul, Farooq Zaman, Naif R. Aljohani, Jia Zhu, Saeed-Ul Hassan, Raheel Nawaz, Ali R Ansari, Muhammad A B Fayyaz

Journal of Informatics and Web Engineering

The present work re-evaluates the evaluation method for text summarization tasks. Two state-of-the-art assessment measures e.g., Recall-Oriented Understudy for Gisting Evaluation (ROUGE) and Bilingual Evaluation Understudy (BLEU) are discussed along with their limitations before presenting a novel evaluation metric. The evaluation scores are significantly different because of the length and vocabulary of the sentences, this suggests that the primary restriction is its inability to preserve the semantics and meaning of the sentences and consistent weight distribution over the whole sentence. To address this, the present work organizes the phrases into six different groups and to evaluate “text summarization” problems, a …

Treatment Recommendation Using Bert Personalization, J Jayapradha, Yukta Kulkarni, Lakshmi Vadhanie G, Palanichamy Naveen, Elham Abdulwahab Anaam

Journal of Informatics and Web Engineering

This research work develops a new framework that combines patient feedback with evidence-based best practices across disease states to improve drug recommendations. It employs BERT as its free-text processing engine to deal with sentiment judgment and classification. The functionality of the system, named `PharmaBERT`, includes acceptance of drug review data as a comprehensive input, drug categorization when dealing with a wide range of treatments and fine-tuning the BERT-based model for gaining positive or negative sentiment towards specific medications. PharmaBERT categorizes various drugs and fine-tunes the BERT structure to perceive lots of possible sentiments for specific medications. Consequently, PharmaBERT brings all …

Performance Evaluation On E-Commerce Recommender System Based On Knn, Svd, Coclustering And Ensemble Approaches, Wan-Er Kong, Tong-Ern Tai, Palanichamy Naveen, Heru Agus Santoso

Journal of Informatics and Web Engineering

E-commerce recommender systems (RS) nowadays are essential for promoting products. These systems are expected to offer personalized recommendations for users based on the user preference. This can be achieved by employing cutting-edge technology such as artificial intelligence (AI) and machine learning (ML). Tailored recommendations for users can boost user experience in using the application and hence increase income as well as the reputation of a company. The purpose of this study is to investigate popular ML methods for e-commerce recommendation and study the potential of ensemble methods to combine the strengths of individual approaches. These recommendations are derived from a …

Comparative Evaluation Of Machine Learning Models For Mobile Phone Price Prediction: Assessing Accuracy, Robustness, And Generalization Performance, Saima Anwar Lashari, Muhammad Muntazir Khan, Abdullah Khan, Sana Salahuddin, Muhammad Noman Ata

Journal of Informatics and Web Engineering

These days, mobile phones are the most commonly purchased goods. Thousands of new models with improved features, designs, and specifications are released yearly. An autonomous mobile price prediction system is required to assist customers in determining whether or not they can afford these devices. Many machine learning models exhibit varying performance degrees based on their architecture and learning properties. Ten widely used classifiers were assessed in this study: Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Decision Tree (DT), Naïve Bayes (NB), Linear Discriminant Analysis (LDA), AdaBoost, and Light Gradient Boosting …

Investigation On Understanding The Numeracy Capacity Of Intellectual Disabled Students Using Enabling Technology Tools: Web Application, Ar And Ui/Ux, Vishvjit Thakar, Romany Thakar, Pratik Vyas

Journal of Informatics and Web Engineering

The population of individuals with intellectual disabilities (ID) is increasing, necessitating assistance with a wide range of daily activities. Acquiring and assessing numeracy and communication skills are critical for this demographic, requiring tools and techniques tailored to their specific needs. Effective educational tools must employ multi-modal and multi-sensory approaches to cater to diverse learning styles and incorporate assistive technological solutions. Despite the availability of numerous tools, there is a need to enhance their utility and effectiveness. This study aims to identify and refine the requirements for an innovative educational tool that employs Two-dimensional (2D) and Augmented Reality (AR) technologies. To …

Ddos Attack Detection With Machine Learning, Wei-Wu Tay, Siew-Chin Chong, Lee-Ying Chong

Journal of Informatics and Web Engineering

Nowadays, Distributed Denial of Service (DDoS) attacks are a major issue in internet security. These attacks target servers or network infrastructure. Similar to an unanticipated traffic jam on highway (lagging/crash) that prevent normal traffic reach to destination. DDoS may prevent users to access any system services. Researchers and scientists have developed numerous methods and algorithms to improve the performance of DDoS detection. In this paper, a DDoS detection method utilizing machine learning is proposed. There are three type of supervised machine learning classification methods which are K-Nearest Neighbor, Multilayer Perceptron and Random Forest, are applied in the proposed work to …

Editorial: Artificial Intelligence And Cybersecurity In Pervasive Computing, Ji-Jian Chin

Journal of Informatics and Web Engineering

Pervasive computing, or ubiquitous computing, is rapidly increasing in capacity and capabilities. With the Internet of Things (IoT) becoming an integral part of daily life and the growing availability of edge computing resources, automation guided by data is advancing applications in healthcare, manufacturing, automotive, and other areas. It's natural that pervasive computing will intersect with artificial intelligence (AI) and cybersecurity. AI can improve detection, prediction, and anticipative responses to human needs, while cybersecurity addresses topics like misuse prevention, ethics, policies, and governance. This issue features seven articles on these intersections, including four AI articles exploring natural language processing and computer …

Intelligent Abstractive Summarization Of Scholarly Publications With Transfer Learning, Farooq Zaman, Munaza Afzal, Pin Shen Teh, Raheem Sarwar, Faisal Kamiran, Naif R. Aljohani, Raheel Nawaz, Muhammad Umair Hassan, Fahad Sabah

Journal of Informatics and Web Engineering

Intelligent abstractive text summarization of scholarly publications refers to machine-generated summaries that capture the essential ideas of an article while maintaining semantic coherence and grammatical accuracy. As information continues to grow at an overwhelming rate, text summarization has emerged as a critical area of research. In the past, summarization of scientific publications predominantly relied on extractive methods. These approaches involve selecting key sentences or phrases directly from the original document to create a summary or generate a suitable title. Although extractive methods preserve the original wording, they often lack the ability to produce a coherent, concise, and fluent summary, especially …

Conditional Deployable Biometrics: Matching Periocular And Face In Various Settings, Jihyeon Kim, Tiong-Sik Ng, Andrew Beng Jin Teoh

Journal of Informatics and Web Engineering

In this paper, we introduce the concept of Conditional Deployable Biometrics (CDB), designed to deliver consistent performance across various biometric matching scenarios, including intra-modal, multimodal, and cross-modal applications. The CDB framework provides a versatile and deployable biometric authentication system that ensures reliable matching regardless of the biometric modality being used. To realize this framework, we have developed CDB-Net, a specialized deep neural network tailored for handling both periocular and face biometric modalities. CDB-Net is engineered to handle the unique challenges associated with these different modalities while maintaining high accuracy and robustness. Our extensive experimentation with CDB-Net across five diverse and …

Front-End Computational Modeling And Design For The Antarctic Demonstrator For The Advanced Particle-Astrophysics Telescope, Marion Sudvarg, Ye Htet, Roger Chamberlain, Jeremy Buhler, Blake Bal, Blake Bal, Corrado Altomare, Corrado Altomare, Davide Serini, Davide Serini, Mario Nicola Mazziotta, Mario Nicola Mazziotta, Leonardo Di Venere, Leonardo Di Venere, Wenlei Chen, Wenlei Chen, James H. Buckley, Roger D. Chamberlain

Computer Science Faculty Research & Creative Works

The Advanced Particle-astrophysics Telescope (APT) is a planned space-based observatory designed to localize MeV to TeV transients such as gamma-ray bursts in real time using onboard computational hardware. The Antarctic Demonstrator for APT (ADAPT) is a prototype high-altitude balloon mission scheduled to fly during the 2025–26 season. Gamma-ray-induced scintillations in CsI tiles will be captured by perpendicular arrays of optical fibers running across both tile surfaces, as well as SiPM-based edge detectors to improve light collection and calorimetry. Signal samples are captured by analog waveform digitizer ASICs then sent to the front end of the computational pipeline, which is designed …

Prompt And Accurate Grb Source Localization Aboard The Advanced Particle Astrophysics Telescope (Apt) And Its Antarctic Demonstrator (Adapt), Ye Htet, Marion Sudvarg, Jeremy Buhler, Roger Chamberlain, Wenlei Chen, James Buckley, Roger D. Chamberlain, Corrado Altomare, Matthew Andrew, Blake Bal, Richard G. Bose, Dana Braun, Eric Burns, Michael L. Cherry, Leonardo Di Venere, Jeffrey Dumonthier, Manel Errando, Stefan Funk

Computer Science Faculty Research & Creative Works

We characterize the performance of our computational pipeline for real-time gamma-ray burst (GRB) detection and localization aboard the Advanced Particle-astrophysics Telescope (APT) – a space-based observatory for MeV to TeV gamma-ray astronomy – and its smaller, balloon-borne prototype, the Antarctic Demonstrator for APT (ADAPT), whose scientific focus will be the detection of MeV transients. These instruments observe scintillation light from multiple Compton scattering and photoabsorption of gamma-ray photons across a series of CsI detector layers. We infer the incident angle of each photon's first scattering to localize its source direction to a Compton ring about the vector defined by its …

Pulling Up Stakes: Migrating Digital Collections From Contentdm To Digital Commons, Adam C. Northam

Velma K. Waters Library Faculty Publications

No abstract provided.

Open-Source Forensics Tools Are Great Tools For Critical Used Machines, Erik Herrera

Electronic Theses and Dissertations

Open-Source software exists on everything from operating systems to daily productivity applications. In digital forensics, a very popular tool that is used to learn on and expand is Autopsy. Autopsy is known in the digital world due to its potential and wide usage. It is in many built packages of software inside the open-source world of applications. It is built into premade operating systems that are involved in Digital Forensics and Penetration Testing. Prebuilt OS includes Kali Linux and Computer Aided Investigative Environment (CAINE).

In the application to defend Open-Source software being just as good as closed-source software, I will …

The Institutional Challenges Of A Quantified Self Study: An Attempt To Ascertain How Data Collected From A Mobile Device Can Be An Indicator Of Personal Mental Health Over Time, Julian Lazaras

University Honors Theses

The adoption of an application of new technology always comes with a bias, this is never more true for the case of human behavioral analytics within higher education. While movements such as the quantified self movement make strides to reinterpret the realm of data analytics, psychology, and computer science, there are inevitably limitations to the adoption and application of such approaches within the standard realm of research. Herein is presented a case where an effort to evaluate the prospect of use of mobile phone data as secondary indicators of personal mental health through the lens of data analysis was put …