Other Computer Sciences Commons^™

A Bilingual Immersive Environment For Kids Learning, Tufail Ahmad, Arifa Bibi, Umar Farooq, Ihsan Rabbi

Journal of Informatics and Web Engineering

Virtual Worlds (VWs) provide an improved learning experience as they have an increased level of user participation and technological involvement. They avoid content duplication and following a set of predefined rules to overcome the issues in game environments. VWs are coherent, persistent and collaborative social places that believe in realism through immersion. Client viewer software is used for user immersion in the form of avatars and in-world content creation in these environments. This work presents a simple bi-lingual environment developed for kids’ learning in English and Urdu using the well-known VW development framework called OpenSimulator (OpSim). It used Firestorm viewer …

Performance Evaluation Of Yolo Models In Plant Disease Detection, Usman Ali, Maizatul Akmar Ismail, Riyaz Ahamed Ariyaluran Habeeb, Syed Roshaan Ali Shah

Journal of Informatics and Web Engineering

Plant diseases significantly impact global agriculture, leading to substantial production losses and economic consequences. Timely disease detection can enhance crop yield, optimize resource utilization, reduce costs, and mitigate environmental effects, ultimately ensuring high-quality food production. Deep learning, specifically computer vision-based techniques, have proven invaluable in tasks like image classification, segmentation, and object detection. Deep Learning techniques such as You Only Look Once (YOLO) models are state of the art neural network algorithms used for accurate object detection. In this study, YOLOv5, YOLOv7 and YOLOv8 models were trained on CCL’20 dataset for citrus disease detection. Data augmentation techniques such as image …

Unveiling The Efficacy Of Ai-Based Algorithms In Phishing Attack Detection, Tajamul Shahzad, Kashif Aman

Journal of Informatics and Web Engineering

Phishing poses a significant challenge in an ever-evolving world. The increased usage of the Internet has resulted in the emergence of a different kind of theft referred to as cybercrime. The term cybercrime describes the act of invading privacy and illegitimately obtaining personal information using digital platform. Primarily an approach named phishing is employed, which involves the use of spoof emails or bogus websites by the attackers to get the victim's personal information like their account credentials, debit, or credit card’s number, etc. To give the brief knowledge of phishing attacks and their types of the objective of this work …

Ensemble Learning-Powered Url Phishing Detection: A Performance Driven Approach, Shougfta Mushtaq, Tabassum Javed, Mazliham Mohd Su’Ud

Journal of Informatics and Web Engineering

With the rapid growth in the usage of the Internet, criminals have found new ways to engage in cyber-attacks. The most common and widespread attack is URL phishing. The proposed system focuses on improving phishing website detection using feature selection and ensemble learning. This model uses two datasets, DS-30 and DS-50, each with 30 and 50 features. Ensemble learning using a voting classifier was then applied to train the model, achieving more accuracy. The combination of HEFS with random forest distribution achieved 94.6% accuracy while minimizing the number of features used (20.8% of the base feature set). The classifier works …

Wix For Web Development And The Application Of The Waterfall Model And Project Based Learning For Project Completion: A Case Study, Mawar Madiah, Ng Kai Xuen, Tan Yew Wen, Tan Zhi Heng, Chong Zhi Tian, Chan Jia Xuan

Journal of Informatics and Web Engineering

Website development without prior knowledge of HTML or programming experience would be a significant challenge. This study aims to share the students' experiences developing a website using Wix, a user-friendly website builder. The website was assigned as a project-based assessment of one of the courses required to complete a Foundation programme. The course was delivered using a project-based learning (PBL) approach in this context. The students worked as a group to write a project proposal, plan activities, develop the website, write reports, and present the outcome. As for the website development process, the study demonstrates the completion of the project …

Empirical Analysis Of Ci/Cd Tools Usage In Github Actions Workflows, Adam Rafif Faqih, Alif Taufiqurrahman, Jati H. Husen, Mira Kania Sabariah

Journal of Informatics and Web Engineering

As software systems grow larger and more complex, with rapidly changing requirements, manually managing code integration, testing, and deployment becomes extremely challenging. Continuous Integration and Continuous Deployment (CI/CD) practices and tools have emerged to help automate these processes. This research explores the usage of different categories of CI/CD tools within GitHub Actions workflow configurations across GitHub repositories. The five-tool categories analyzed are Version Control Management, Static Code Analysis, Build Automation, Test Automation, and CI/CD Servers. The data used in this research is from a dataset of GitHub Actions workflow configuration files. From the data, the usage is extracted and the …

Streamlining Dental Clinic Management For Effective Digitisation Productivity And Usability, Sin-Ban Ho, En-Yu Chew, Chuie-Hong Tan

Journal of Informatics and Web Engineering

Oral health is an integral part of overall health, and poor oral hygiene can lead to a variety of health problems. Modern oral care has greatly improved our quality of life, but the increasing demand for routine dental checkups and treatments calls for improved systems for managing patient records and appointments. While technology has significantly enhanced the efficacy and experience of dental care, many dental clinics still rely on paper records to record the patient’s oral condition, but these are not easily accessible to the patients for viewing. This study aims to address the issue by developing a Dental Clinic …

Knowledge-Based Word Tokenization System For Urdu, Asif Khan, Khairullah Khan, Wahab Khan, Sadiq Nawaz Khan, Rafiul Haq

Journal of Informatics and Web Engineering

Word tokenization, a foundational step in natural language processing (NLP), is critical for tasks like part-of-speech tagging, named entity recognition, and parsing, as well as various independent NLP applications. In our tech-driven era, the exponential growth of textual data on the World Wide Web demands sophisticated tools for effective processing. Urdu, spoken widely across the globe, is experiencing a surge in, presents unique challenges due to its distinct writing style, the absence of capitalization features, and the prevalence of compound words. This study introduces a novel knowledge-based word tokenization system tailored for Urdu. Central to this system is a maximum …

Social Messaging Application With Translation And Speech-To-Text Transformation, Kang Qin Yip, Pey Yun Goh, Lee Ying Chong

Journal of Informatics and Web Engineering

Unlike traditional SMS or MMS, messaging apps offer a broader range of data transmission capabilities. The application utilizes a WIFI or internet connection and enables users to exchange information through various means such as text, voice, and multimedia files. However, popular messaging applications such as WeChat, Telegram, and WhatsApp have limitations in language translation and file uploading size. Thus, this project aims to address these limitations by developing a social messaging application that serves as a comprehensive communication tool. The application will facilitate both written and verbal communication by providing translation services for various languages, including voice messages. The proposed …

Machine Learning Multimodal Framework For Fake News Detection And Mitigation, Nada A. Gaballah

Theses and Dissertations

Social media has become our new reality, people wake up every morning and the first thing they do before getting out of bed, is check their social media. Nowadays, people rarely read newspapers, they even rarely watch TV news or listen to radio broadcasts. In recent years, we have witnessed lots of fake news roaming social media every second, with people simply believing it and spreading it even more without checking the credibility of this news. This fake news affected several domains like what happened in the US election in 2016 and again in 2020, the false information about Covid-19 …

Combining Cloud Architecting With Education, Sharon P. Pagidipati

Liberal Arts and Engineering Studies

I pursued the AWS Solutions Architect Professional Certification while applying my knowledge to build and revise technical solutions for an educational company known as EDFX.

Context In Computer Vision: A Taxonomy, Multi-Stage Integration, And A General Framework, Xuan Wang

Dissertations, Theses, and Capstone Projects

Contextual information has been widely used in many computer vision tasks, such as object detection, video action detection, image classification, etc. Recognizing a single object or action out of context could be sometimes very challenging, and context information may help improve the understanding of a scene or an event greatly. However, existing approaches design specific contextual information mechanisms for different detection tasks.

In this research, we first present a comprehensive survey of context understanding in computer vision, with a taxonomy to describe context in different types and levels. Then we proposed MultiCLU, a new multi-stage context learning and utilization framework, …

Toward Intuitive 3d Interactions In Virtual Reality: A Deep Learning- Based Dual-Hand Gesture Recognition Approach, Trudi Di Qi, Franceli L. Cibrian, Meghna Raswan, Tyler Kay, Hector M. Camarillo-Abad, Yuxin Wen

Engineering Faculty Articles and Research

Dual-hand gesture recognition is crucial for intuitive 3D interactions in virtual reality (VR), allowing the user to interact with virtual objects naturally through gestures using both handheld controllers. While deep learning and sensor-based technology have proven effective in recognizing single-hand gestures for 3D interactions, research on dual-hand gesture recognition for VR interactions is still underexplored. In this work, we introduce CWT-CNN-TCN, a novel deep learning model that combines a 2D Convolution Neural Network (CNN) with Continuous Wavelet Transformation (CWT) and a Temporal Convolution Network (TCN). This model can simultaneously extract features from the time-frequency domain and capture long-term dependencies using …

Toward The Integration Of Behavioral Sensing And Artificial Intelligence, Subigya K. Nepal

Dartmouth College Ph.D Dissertations

The integration of behavioral sensing and Artificial Intelligence (AI) has increasingly proven invaluable across various domains, offering profound insights into human behavior, enhancing mental health monitoring, and optimizing workplace productivity. This thesis presents five pivotal studies that employ smartphone, wearable, and laptop-based sensing to explore and push the boundaries of what these technologies can achieve in real-world settings. This body of work explores the innovative and practical applications of AI and behavioral sensing to capture and analyze data for diverse purposes. The first part of the thesis comprises longitudinal studies on behavioral sensing, providing a detailed, long-term view of how …

Automated Cinematographer For Vr Viewing Experiences, Zihan Wu

Dartmouth College Master’s Theses

As the virtual reality (VR) industry continues to evolve, the question of how to effectively capture VR experiences for an audience remains a challenge. The predominant method of showcasing VR applications through first-person recordings lacks cinematic interest, failing to capture other viewpoints and the essence of the moment. Meanwhile, manually setting up cameras and editing videos requires technical expertise on behalf of the user. In this paper, we propose the use of machine learning (ML) to automatically select the most compelling predefined viewpoint in a VR environment, at any given moment. Our models, trained on actor motion and voice volume, …

Comparative Predictive Analysis Of Stock Performance In The Tech Sector, Asaad Sendi

LSU New Orleans Theses and Dissertations

This study compares the performance of deep learning models, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Transformer, in predicting stock prices across five companies (AAPL, CSCO, META, MSFT, and TSLA) from July 2019 to July 2023. Key findings reveal that GRU models generally exhibit the lowest Mean Absolute Error (MAE), indicating higher precision, particularly notable for CSCO with a remarkably low MAE. While LSTM models often show slightly higher MAE values, they outperform Transformer models in capturing broader trends and variance in stock prices, as evidenced by higher R-squared (R2) values. Transformer models generally exhibit higher MAE …

Choreographing The Rhythms Of Observation: Dynamics For Ranged Observer Bipartite-Unipartite Spatiotemporal (Robust) Networks, Edward A. Holmberg Iv

LSU New Orleans Theses and Dissertations

Existing network analysis methods struggle to optimize observer placements in dynamic environments with limited visibility. This dissertation introduces the novel ROBUST (Ranged Observer Bipartite-Unipartite SpatioTemporal) framework, offering a significant advancement in modeling, analyzing, and optimizing observer networks within complex spatiotemporal domains. ROBUST leverages a unique bipartite-unipartite approach, distinguishing between observer and observable entities while incorporating spatial constraints and temporal dynamics.

This research extends spatiotemporal network theory by introducing novel graph-based measures, including myopic degree, spatial closeness centrality, and edge length proportion. These measures, coupled with advanced clustering techniques like Proximal Recurrence, provide insights into network structure, resilience, and the effectiveness …

Cardiogpt: An Ecg Interpretation Generation Model, Guohua Fu, Jianwei Zheng, Islam Abudayyeh, Chizobam Ani, Cyril Rakovski, Louis Ehwerhemuepha, Hongxia Lu, Yongjuan Guo, Shenglin Liu, Huimin Chu, Bing Yang

Mathematics, Physics, and Computer Science Faculty Articles and Research

Numerous supervised learning models aimed at classifying 12-lead electrocardiograms into different groups have shown impressive performance by utilizing deep learning algorithms. However, few studies are dedicated to applying the Generative Pre-trained Transformer (GPT) model in interpreting electrocardiogram (ECG) using natural language. Thus, we are pioneering the exploration of this uncharted territory by employing the CardioGPT model to tackle this challenge. We used a dataset of ECGs (standard 10s, 12-channel format) from adult patients, with 60 distinct rhythms or conduction abnormalities annotated by board-certified, actively practicing cardiologists. The ECGs were collected from The First Affiliated Hospital of Ningbo University and Shanghai …

Sentiment Analysis Using Support Vector Machine And Random Forest, Talha Ahmed Khan, Rehan Sadiq, Zeeshan Shahid, Muhammad Mansoor Alam, Mazliham Bin Mohd Su'ud

Journal of Informatics and Web Engineering

Sentiment analysis, is commonly known as opinion mining, is a vital field in natural language processing (NLP) that claims to find out the sentiment or emotion expressed in a given text. This research paper demonstrates an exhaustive survey of sentiment analysis, focusing on the application of machine learning techniques. Comprehensive parametric literature review has been completed to determine the sentiment analysis using SVM and Random Forest. Additionally, the paper covers preprocessing techniques, feature extraction, model training, evaluation, and challenges encountered in sentiment analysis. The findings of this research contribute to a deeper understanding of sentiment analysis and provide insights into …

Emojis And Miscommunication In Text-Based Interactions Among Nigerian Youths, Uduak Udoudom, Godwin William, Anthony Igiri, Ememobong Okon, Kalita Aruku

Journal of Informatics and Web Engineering

This paper explores the dynamic role of emojis in text-based communication among Nigerian youths and the potential implications for miscommunication. Emojis have become integral to contemporary digital conversations, offering users a visual means of expressing emotions, tone, and context within the constraints of text-based interactions. In the context of Nigeria, a country with a diverse linguistic landscape and a youthful population heavily engaged in online communication, understanding the impact of emojis on interpersonal exchanges becomes particularly pertinent. This paper examines the prevalence and patterns of emoji usage among Nigerian youths across various digital platforms. It investigates the cultural nuances and …

Optimizing Medical Iot Disaster Management With Data Compression, Nunudzai Mrewa, Athirah Mohd Ramly, Angela Amphawan, Tse Kian Neo

Journal of Informatics and Web Engineering

In today's technological landscape, the convergence of the Internet of Things (IoT) with various industries showcases the march of progress. This coming together involves combining diverse data streams from different sources and transmitting processed data in real-time. This empowers stakeholders to make quick and informed decisions, especially in areas like smart cities, healthcare, and industrial automation, where efficiency gains are evident. However, with this convergence comes a challenge – the large amount of data generated by IoT devices. This data overload makes processing and transmitting information efficiently a significant hurdle, potentially undermining the benefits of this union. To tackle this …

Editorial Preview, Su-Cheng Haw

Journal of Informatics and Web Engineering

This editorial highlights all 19 papers in the February issue that deal with the practical aspects of Machine Learning (ML), Artificial Intelligence (AI), Data Mining (DM), the Internet of Things (IoT), and other topics in Computer Science. This issue also includes suggestions for several worthwhile works that deserve further research. With effective from this volume, we will be publishing triannually in our February, June and October issues.

Term Standardisation With Lda Model To Detect Service Disruption Events Using English And Manglish Tweets, Noraysha Yusuf, Maizatul Akmar Ismail, Tasnim M.A. Zayet, Kasturi Dewi Varathan, Rafidah Md Noor

Journal of Informatics and Web Engineering

Rapid transit is one of Malaysia's most important transportation modes, where commuters use public transportation to travel. Any disruption in the rapid transit service affects their daily routines. Therefore, detecting such service disruption has become fundamental. In this study, the disruption in Malaysia's rapid transit service was assessed using English and Manglish (a combination of English and Malay) tweets through Latent Dirichlet Allocation (LDA). The gathered tweets were classified into event and non-event tweets and LDA was applied to the event tweets. Manglish event tweets were pre-processed using the proposed term standardisation technique. As a result, LDA has proved its …

Modelling Of Virtual Campus Tour In Minecraft, Liyana Tan Lin, Han-Foon Neo

Journal of Informatics and Web Engineering

Virtual tours have revolutionized the way to explore and experience places from the comfort of our own home. Through advanced technology and immersive digital platforms, virtual tours offer a compelling alternative to tradition face-to-face visits. Whether a famous landmark, museum, real estate or natural wonders, virtual tours offer a unique opportunity to navigate and discover these places form a distance. Meanwhile, creating a virtual tour in Minecraft can provide a unique and immersive experience that sets the users apart from other virtual tour platforms. Minecraft is one of the most popular video games in the world and boasts a large …

Implementation Of Grover’S Algorithm & Bernstein-Vazirani Algorithm With Ibm Qiskit, Yang-Che Liu, Mei-Feng Liu

Journal of Informatics and Web Engineering

Quantum logic gates differ from classical logic gates as the former involves quantum operators. The conventional gates such as AND, OR, NOT etc., are generally classified as classical gates, however, some of the quantum gates are known as Pauli gates, Toffoli gates and Hadamard gates, respectively. Normally classical states only involve 0 and 1, whereas quantum states involve the superpositions of 0 and 1. Hence, underlying principles of algorithm implementation for classical logic gate and quantum logic gate are indeed different. In this paper, we introduce significant concepts of quantum computations, analyse the discrepancy between classical and quantum gates, compare …

A Campus-Based Chatbot System Using Natural Language Processing And Neural Network, Tuan-Jun Goh, Lee-Ying Chong, Siew-Chin Chong, Pey-Yun Goh

Journal of Informatics and Web Engineering

A chatbot is designed to simulate human conversation and provide instant responses to users. Chatbots have gained popularity in providing automated customer support and information retrieval among organisations. Besides, it also acts as a virtual assistant to communicate with users by delivering updated answers based on users' input. Most chatbots still use the traditional rule-based chatbot, which can only respond to pre-defined sentences, making the users unlikely to use the chatbot. This paper aims to design and build a campus chatbot for the Faculty of Information Science & Technology (FIST) of Multimedia University that facilitates the study life of FIST …

Personalized Healthcare: A Comprehensive Approach For Symptom Diagnosis And Hospital Recommendations Using Ai And Location Services, Seng-Keong Tan, Siew-Chin Chong, Kuok-Kwee Wee, Lee-Ying Chong

Journal of Informatics and Web Engineering

Utilizing digital advancements, an integrated Flask-based platform has been engineered to centralize personal health records and facilitate informed healthcare decisions. The platform utilizes a Random Forest model-based symptom checker and an OpenAI API-powered chatbot for preliminary disease diagnosis and integrates Google Maps API to recommend proximal hospitals based on user location. Additionally, it contains a comprehensive user profile encompassing general information, medical history, and allergies. The system includes a medicine reminder feature for medication adherence. This innovative amalgamation of technology and healthcare fosters a user-centric approach to personal health management.

Vision-Based Gait Analysis For Neurodegenerative Disorders Detection, Vincent Wei Sheng Tan, Wei Xiang Ooi, Yi Fan Chan, Tee Connie, Michael Kah Ong Goh

Journal of Informatics and Web Engineering

Parkinson’s Disease (PD) is a debilitating neurodegenerative disorder that affects a significant portion of aging population. Early detection of PD symptoms is crucial to prevent the progression of the disease. Research has revealed that gait attributes can provide valuable insights into PD symptoms. The gait acquisition techniques used in current research can be broadly divided into two categories: vision-based and sensor-based. The markerless vision-based classification model has become a prominent research trend due to its simplicity, low cost and patient comfort. In this study, we propose a novel markerless vision-based approach to obtain gait features from participants' gait videos. A …

Adaptive Gaussian Wiener Filter For Ct-Scan Images With Gaussian Noise Variance, Kai Liang Lew, Chung Yang Kew, Kok Swee Sim, Shing Chiang Tan

Journal of Informatics and Web Engineering

Medical imaging plays an important role in modern healthcare, with Computed Tomography (CT) being essential for high-resolution cross-sectional imaging. However, Gaussian noise often occurs within the CT scan images and makes it difficult for image interpretation and reduces the diagnostic accuracy, creating a significant obstacle to fully utilizing CT scanning technology. Existing denoising techniques have a hard time balance between noise reduction and preserving the important image details, failing to enable the optimal diagnostic precision. This study introduces Adaptive Gaussian Wiener Filter (AGWF), a novel filter aims to denoise CT scan images that have been corrupted with various Gaussian noise …

Comparison Of Machine Learning Methods For Calories Burn Prediction, Alfred Tan Jing Sheng, Zarina Che Embi, Noramiza Hashim

Journal of Informatics and Web Engineering

This paper focuses on the prediction of calories burned during exercise using machine learning techniques. Due to a growing number of obesity and overweight people, a healthy lifestyle must be adopted and maintained. This study explores and compares several machine learning regression models namely LightGBM, XGBoost, Random Forest, Ridge, Linear, Lasso, and Logistic to assess their calories burned prediction performance that can be used in systems such as fitness recommender systems supporting a healthy lifestyle. Our findings show that the LightGBM for predicting calorie burn has a good accuracy of 1.27 mean absolute error, giving users reliable recommendations. The proposed …