Software Engineering Commons^™

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 …

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 …

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 …

Plant Disease Detection And Classification Using Deep Learning Methods: A Comparison Study, Pei-Wern Chin, Kok-Why Ng, Naveen Palanichamy

Journal of Informatics and Web Engineering

The presence issue of inaccurate plant disease detection persists under real field conditions and most deep learning (DL) techniques still struggle to achieve real-time performance. Hence, challenges in choosing a suitable deep-learning technique to tackle the problem should be addressed. Plant diseases have a detrimental effect on agricultural yield, hence early detection is crucial to prevent food insecurity. To identify and categorise the indications of plant diseases, numerous developed or modified DL architectures are utilised. This paper aims to observe the performance of the YOLOv8 model, which has better performance than its predecessors, on a small-scale plant disease dataset. This …

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 …

Goholiday: Development Of An Improvised Mobile Application For Boutique Hotels And Resorts, Iftiaj Alom, Ismail Ahmed Al-Qasem Al-Hadi, Neesha Jothi, Sook Fern Yeo

Journal of Informatics and Web Engineering

One of the main challenges boutique hotels and resorts face is the direct outreach to tourists and customers. As a result, these independent hotels often resort to online platforms such as Agoda and Airbnb to expand their customer base. However, this approach comes at the cost of losing revenue to Online Travel Agencies (OTAs) that solely focus on room sales, hindering the establishment of a strong brand image for boutique hotels and resorts. Considering the heavy reliance on OTAs, this paper focuses on the development of GoHoliday, a cross-platform mobile app prototype that aims to bridge the gap between boutique …

Weather-Based Arthritis Tracking: A Mobile Mechanism For Preventive Strategies, Jin-Lun Goh, Sin-Ban Ho, Chuie-Hong Tan

Journal of Informatics and Web Engineering

Arthritis is a common joint disorder characterised by symptoms such as swelling, pain, stiffness, and limited joint movement. It primarily affects older individuals, women, and athletes. The advent of information technology has created opportunities for patients to manage their health conditions more effectively. Research indicates that weather can affect arthritis symptoms, with many patients experiencing severe discomfort during rainy weather due to the expansion of already inflamed tissues. However, there is currently no mobile application mechanism available that combines weather forecasting with health recommendations for arthritis patients, which means that patients may not have access to important information that could …

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 …

Prediction Of Student’S Academic Performance Through Data Mining Approach, Muhammad Mubashar Hussain, Shahzad Akbar, Syed Ale Hassan, Muhammad Waqas Aziz, Farwa Urooj

Journal of Informatics and Web Engineering

The universities and institutes produce a large amount of student data that can be used in a disciplinary way and useful information can be extracted by using an automated approach. Educational Data Mining (EDM) is an emerging discipline used in the educational environment to deal with big student data and extract useful information. The data mining of students’ data can help the At-risk students as well as the stakeholders by the early warning. This study aims to predict the performance of the students based on student-related data to increase the overall performance. In existing studies, insufficient attributes and complexity of …

Hybrid Crow Search And Rbfnn: A Novel Approach To Medical Data Classification, Marai Ali, Faisal Khan, Muhammad Nouman Atta, Abdullah Khan, Asfandyar Khan

Journal of Informatics and Web Engineering

The Radial Basis Function Neural Network (RBFNN) is frequently employed in artificial neural networks for diverse classification tasks, yet it encounters certain limitations, including issues related to network latency and local minima. To tackle these challenges, researchers have explored various algorithms to enhance learning performance and alleviate local minima problems. This study introduces a novel approach that integrates the Crow Search Algorithm (CSA) with RBFNN to augment the learning process and address the local minima issue associated with RBFNN. The study evaluates the performance of this innovative model by comparing it to state-of-the-art models like Flower-pollination-RBNN (FP-NN), Artificial Neural Network …

Electric Vehicle Health Monitoring With Electric Vehicle Range Prediction And Route Planning, Jayapradha Jayaram, J Chetan, Barun Nayak

Journal of Informatics and Web Engineering

The automotive industry is experiencing a revolutionary wave due to the rapid spread of electric vehicles (EVs), which is paving the way for a fundamental and long-lasting revolution in the way we approach transportation. The global movement to reduce greenhouse gas emissions and lessen the environmental impact of traditional internal combustion engine vehicles has seen a significant boost in the popularity of electric vehicles as people come together to support environmentally conscious and sustainable mobility solutions. But the ecology surrounding electric vehicles must continue to flourish if the particular problems that EVs present are to be successfully addressed. Chief among …

An In-Depth Analysis On Efficiency And Vulnerabilities On A Cloud-Based Searchable Symmetric Encryption Solution, Prithvi Chaudhari, Ji-Jian Chin, Soeheila Moesfa Bt Mohamad

Journal of Informatics and Web Engineering

Searchable Symmetric Encryption (SSE) has come to be as an integral cryptographic approach in a world where digital privacy is essential. The capacity to search through encrypted data whilst maintaining its integrity meets the most important demand for security and confidentiality in a society that is increasingly dependent on cloud-based services and data storage. SSE offers efficient processing of queries over encrypted datasets, allowing entities to comply with data privacy rules while preserving database usability. Our research goes into this need, concentrating on the development and thorough testing of an SSE system based on Curtmola’s architecture and employing Advanced Encryption …

Better Pay Attention Whilst Fuzzing, Shunkai Zhu, Jingyi Wang, Jun Sun, Jie Yang, Xingwei Lin, Tianyi Wang, Liyi Zhang, Peng Cheng

Research Collection School Of Computing and Information Systems

Fuzzing is one of the prevailing methods for vulnerability detection. However, even state-of-the-art fuzzing methods become ineffective after some period of time, i.e., the coverage hardly improves as existing methods are ineffective to focus the attention of fuzzing on covering the hard-to-trigger program paths. In other words, they cannot generate inputs that can break the bottleneck due to the fundamental difficulty in capturing the complex relations between the test inputs and program coverage. In particular, existing fuzzers suffer from the following main limitations: 1) lacking an overall analysis of the program to identify the most “rewarding” seeds, and 2) lacking …

Detecting Outdated Code Element References In Software Repository Documentation, Wen Siang Tan, Markus Wagner, Christoph Treude

Research Collection School Of Computing and Information Systems

Outdated documentation is a pervasive problem in software development, preventing effective use of software, and misleading users and developers alike. We posit that one possible reason why documentation becomes out of sync so easily is that developers are unaware of when their source code modifications render the documentation obsolete. Ensuring that the documentation is always in sync with the source code takes considerable effort, especially for large codebases. To address this situation, we propose an approach that can automatically detect code element references that survive in the documentation after all source code instances have been deleted. In this work, we …

Mutation Analysis For Evaluating Code Translation, Giovani Guizzo, Jie M. Zhang, Federica Sarro, Christoph Treude, Mark Harman

Research Collection School Of Computing and Information Systems

Source-to-source code translation automatically translates a program from one programming language to another. The existing research on code translation evaluates the effectiveness of their approaches by using either syntactic similarities (e.g., BLEU score), or test execution results. The former does not consider semantics, the latter considers semantics but falls short on the problem of insufficient data and tests. In this paper, we propose MBTA (Mutation-based Code Translation Analysis), a novel application of mutation analysis for code translation assessment. We also introduce MTS (Mutation-based Translation Score), a measure to compute the level of trustworthiness of a translator. If a mutant of …

Delving Into Multimodal Prompting For Fine-Grained Visual Classification, Xin Jiang, Hao Tang, Junyao Gao, Xiaoyu Du, Shengfeng He, Zechao Li

Research Collection School Of Computing and Information Systems

Fine-grained visual classification (FGVC) involves categorizing fine subdivisions within a broader category, which poses challenges due to subtle inter-class discrepancies and large intra-class variations. However, prevailing approaches primarily focus on uni-modal visual concepts. Recent advancements in pre-trained vision-language models have demonstrated remarkable performance in various high-level vision tasks, yet the applicability of such models to FGVC tasks remains uncertain. In this paper, we aim to fully exploit the capabilities of cross-modal description to tackle FGVC tasks and propose a novel multimodal prompting solution, denoted as MP-FGVC, based on the contrastive language-image pertaining (CLIP) model. Our MP-FGVC comprises a multimodal prompts …

Broadening Participation Of Teachers In Computing: Examining Postsecondary Educational Experiences And Prospective Educators’ Cs Teaching Interests, Robert Schwarzhaupt, Alexsandra Galanis, Joanna Goode, Kate Blanchard, Jill Bowdon, Joseph P. Wilson

Journal of Computer Science Integration

Teacher shortages in K–12 computer science (CS) education negatively impact students’ access to CS courses, exposure to CS concepts, and interest in CS-related careers. To address CS teacher shortages, this study seeks to understand factors related to expressing a preference to teach CS among prospective teachers. The study team analyzed data from 27,700 prospective teacher applications accepted into the 2016–2020 Teach For America (TFA) corps (cohorts). The TFA corps is an alternative teacher development program that recruits and prepares participants to obtain their teaching certification while they work for at least two years in underserved communities on a temporary teaching …

The Aim To Decentralize Economic Systems With Blockchains And Crypto, Mary Lacity

Arkansas Law Review

As an information systems (“IS”) professor, I wrote this Article for legal professionals new to blockchains and crypto. This target audience likely is most interested in crypto for its legal implications—depending on whether it functions as currencies, securities, commodities, or properties; however, legal professionals also need to understand crypto’s origin, how transactions work, and how they are governed.

Piecing Together Performance: Collaborative, Participatory Research-Through-Design For Better Diversity In Games, Daniel L. Gardner, Louanne Boyd, Reginald T. Gardner

Engineering Faculty Articles and Research

Digital games are a multi-billion-dollar industry whose production and consumption extend globally. Representation in games is an increasingly important topic. As those who create and consume the medium grow ever more diverse, it is essential that player or user-experience research, usability, and any consideration of how people interface with their technology is exercised through inclusive and intersectional lenses. Previous research has identified how character configuration interfaces preface white-male defaults [39, 40, 67]. This study relies on 1-on-1 play-interviews where diverse participants attempt to create “themselves” in a series of games and on group design activities to explore how participants may …

Weed Seed Wizard Case Study - An Early Harvest Versus A Late Harvest, Department Of Primary Industries And Regional Development, Western Australia

Biosecurity research reports

The Weed Seed Wizard is a national collaborative project that uses paddock management information to predict weed emergence and crop losses now and in the future.

The Weed Seed Wizard is a computer simulation tool that:

• applies to all Australian grain growing areas

• helps growers understand and manage weed seedbanks on their farms

• uses farm management records to simulate how different crop rotations, weed control techniques, irrigation, grazing and harvest management tactics can affect weed numbers, the weed seedbank and yields

• uses farm-specific management and site-specific weather

• is multi-species

See www.dpird.wa.gov.au for further information on Weed Seed Wizard.

This case …

Weed Seed Wizard Scenario - Herbicide Resistance In Wild Radish In Moora, Western Australia, Department Of Primary Industries And Regional Development, Western Australia

Biosecurity research reports

The Weed Seed Wizard is a national collaborative project that uses paddock management information to predict weed emergence and crop losses now and in the future.

The Weed Seed Wizard is a computer simulation tool that:

• applies to all Australian grain growing areas

• helps growers understand and manage weed seedbanks on their farms

• uses farm management records to simulate how different crop rotations, weed control techniques, irrigation, grazing and harvest management tactics can affect weed numbers, the weed seedbank and yields

• uses farm-specific management and site-specific weather

• is multi-species

See www.dpird.wa.gov.au for further information on Weed Seed Wizard.

This Western …

Leveraging Software Testing Techniques To Explain, Analyze, And Debug Machine Learning Models, Sunny Shree

Computer Science and Engineering Dissertations - Archive

Machine learning (ML) algorithms are changing many aspects of modern life by analyzing data, identifying patterns, and making predictive decisions across industries such as healthcare, transportation, finance, and e-commerce. However, ML models often operate as "black boxes," making it difficult to interpret their decision-making processes. This lack of transparency creates challenges in testing, debugging, and understanding model behavior, which affects user trust and raises concerns about trustworthiness, accountability, reliability, and fairness in high-stakes applications.

Explainable Artificial Intelligence (XAI) aims to address these challenges by providing tools and methods that explain the decision-making processes of ML models in a way that …

Github Uncovered: Revealing The Social Fabric Of Software Development Communities, Abduljaleel Al Rubaye

Graduate Thesis and Dissertation 2023-2024

The proliferation of open-source software development platforms has given rise to various online social communities where developers can seamlessly collaborate, showcase their projects, and exchange knowledge and ideas. GitHub stands out as a preeminent platform within this ecosystem. It offers developers a space to host and disseminate their code, participate in collaborative ventures, and engage in meaningful dialogues with fellow community members. This dissertation embarks on a comprehensive exploration of various facets of software development communities on GitHub, with a specific focus on innovation diffusion, repository popularity dynamics, code quality enhancement, and user commenting behaviors. This dissertation introduces a popularity-based …

Improving The Accuracy Of Software Models Using Refinement And Mutation Testing, Ana Jovanovic

Computer Science and Engineering Dissertations - Archive

Writing correct software models is important in today’s society. Unfortunately, software development is an error-prone task that frequently leads to buggy software. That is why users, both novices and experts, make use of additional techniques and tools to make software more reliable and correct. One of the languages that proposes a solution to this is Alloy. Alloy is a declarative language based on first order logic. Its main advantage is the ability to describe complex systems using concise formal logic. To verify the model and its properties, Alloy uses the Alloy Analyzer, an SAT-based verification tool that supports fully automatic …

A 3-Step, Open-Data, Ride-Hailing Ridership Model With Pricing Applications, Richard A. Mucci

Theses and Dissertations--Civil Engineering

Researchers and practitioners studied the effects ride-hailing had in cities before the covid-19 pandemic. Previous research found ride-hailing to produce negative externalities, such as reducing transit ridership and increasing congestion in various cities. Since the pandemic, ride-hailing ridership has nearly recovered to pre-pandemic levels in Chicago. Ride-hailing ridership has grown steadily since the pandemic while a rider’s willingness to share their trip stagnated. Ride-hailing ridership nearly recovering to pre-covid levels in Chicago suggests that transportation planners, and policy makers, will need to continue assessing the impacts ride-hailing trips have in their cities.

Pickup and drop off locations in the Chicago …

Cyclistai: A Smartphone Solution For Cyclist Stress Assessment Using Deep Learning, Aairish Singh

Computer Science and Engineering Theses - Archive

Cycling presents a compelling solution for promoting personal health and environmental well-being, particularly for short-distance travel. Despite its numerous advantages, cycling uptake in the United States remains disproportionately low, primarily due to safety concerns. Traditional frameworks for assessing cyclist stress are hindered by their impracticality and inability to provide real-time evaluations. Self-report surveys and physiological measurements offer alternative approaches but suffer from limitations such as retrospective reporting biases and accessibility challenges, respectively. This thesis introduces CyclistAI, a novel smartphone-based cyclist stress assessment model that leverages context sensing. By combining Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) techniques, CyclistAI …