Software Engineering Commons^™

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 …

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 …

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 …

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 …

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 …

Language Models For Rare Disease Information Extraction: Empirical Insights And Model Comparisons, Shashank Gupta

Theses and Dissertations--Computer Science

End-to-end relation extraction (E2ERE) is a crucial task in natural language processing (NLP) that involves identifying and classifying semantic relationships between entities in text. This thesis compares three paradigms for end-to-end relation extraction (E2ERE) in biomedicine, focusing on rare diseases with discontinuous and nested entities. We evaluate Named Entity Recognition (NER) to Relation Extraction (RE) pipelines, sequence-to-sequence models, and generative pre-trained transformer (GPT) models using the RareDis information extraction dataset. Our findings indicate that pipeline models are the most effective, followed closely by sequence-to-sequence models. GPT models, despite having eight times as many parameters, perform worse than sequence-to-sequence models and …

A Self-Supervised Knowledge Distillation Approach To Anomaly Detection In X-Ray Imagery, Kaden Quinn Mceldowney

Graduate Theses, Dissertations, and Problem Reports (ETD)

Many cargo containers enter the United States every day by truck, rail, and sea. As a result of the large number of cargo containers entering the United States, not all of them can be thoroughly inspected. Most of these containers contain properly documented and legal cargo, but some people take advantage of this situation by hiding illicit items in the cargo containers such as drugs. To more efficiently and thoroughly inspect cargo containers, Customs and Border Protection (CBP) uses X-ray imaging machines to obtain images that reveal the interior of cargo containers. These X-ray images must be inspected to ensure …

Adaptive Neh With Constrained Nearest Neighbor Subtours For The Electric Vehicle Routing Problem With Time Windows, Andrew Struthers

All Master's Theses

The development of electric vehicles is currently considered one of the most innovative areas in manufacturing. Largely driven by the desire to reduce greenhouse emissions, electric vehicles are seen as a viable alternative to internal combustion engine cars. Starting from consumer cars, a dedicated effort is being made to translate this into commercial vehicles for freight and delivery. This research introduces a novel adaptive Nawaz, Enscore, Ham (NEH) algorithm with constrained nearest neighbor subtour (NEH-NN). This algorithm is tested on the standard benchmark problems in literature and used as a seed solution for the Genetic Algorithm (GA). The performance and …

Robot-Based 3d Printing, Aaron Hoffman

Williams Honors College, Honors Research Projects

Details of a large-format 3D printer created to print experimental materials, test multi-axis print techniques, and quickly print large objects. The printer consists of a 7-axis robotic arm and pellet extruder, which are controlled by a PC. Experimental materials such as recycled polymers or carbon-fiber reinforced materials can be easily tested with the pellet format of the extruder. The printer can perform different printing techniques and can be used to experiment with material properties when using these techniques with different polymers. The print surface is around 5 times larger than the average commercial 3D printer, and the robotic arm provides …

Media Haze Classification In Retinal Images Using Deep Learning, Jonathan O'Berry

UNF Graduate Theses and Dissertations

Media Haze (MH) is a condition that affects an individual’s quality of life by affecting their eyes. Current practice is to detect MH by manually examining retinal fundus (retinal) images. The analysis of images being used as the prevalent technique for identifying the MH condition strongly suggests that automation of this process may be possible. In recent years, machine learning, specifically computer vision, has allowed for the automation of tasks relating to image analysis. This ability to automate has also recently been shown in the medical field for some eye conditions and diseases. This thesis centers around the problem of …

Conversational Localization: Indoor Human Localization Through Intelligent Conversation, Sheshadri Smitha, Kotaro Hara

Research Collection School Of Computing and Information Systems

We propose a novel sensorless approach to indoor localization by leveraging natural language conversations with users, which we call conversational localization. To show the feasibility of conversational localization, we develop a proof-of-concept system that guides users to describe their surroundings in a chat and estimates their position based on the information they provide. We devised a modular architecture for our system with four modules. First, we construct an entity database with available image-based floor maps. Second, we enable the dynamic identification and scoring of information provided by users through our utterance processing module. Then, we implement a conversational agent that …

Reducing Food Scarcity: The Benefits Of Urban Farming, S.A. Claudell, Emilio Mejia

Journal of Nonprofit Innovation

Urban farming can enhance the lives of communities and help reduce food scarcity. This paper presents a conceptual prototype of an efficient urban farming community that can be scaled for a single apartment building or an entire community across all global geoeconomics regions, including densely populated cities and rural, developing towns and communities. When deployed in coordination with smart crop choices, local farm support, and efficient transportation then the result isn’t just sustainability, but also increasing fresh produce accessibility, optimizing nutritional value, eliminating the use of ‘forever chemicals’, reducing transportation costs, and fostering global environmental benefits.

Imagine Doris, who is …

A Critical Computing Curriculum Design Case: Exploring Tribal Sovereignty For Middle School Students, Kristin A. Searle, Aubrey Rogowski, Colby Tofel-Grehl, Mengying Jiang

Journal of Computer Science Integration

We report on our efforts to design an integrated computing curriculum for middle school students in Montana that is in line with the Kapor Center’s focus on culturally sustaining-revitalizing pedagogies. Montana provides a unique context for doing this work because a state constitutional mandate requires all K-12 students to learn about tribal histories and cultures through Indian Education For All (IEFA). IEFA centers around seven essential understandings about Indigenous peoples in Montana that are integrated across content areas. In addition, implementation of Montana’s CS standards began in the 2021–2022 school year. In the curricular design, we sought to bring together …

Employing An Abolitionist, Critical Race Pedagogy In Cs: Centering The Voices, Experiences And Technological Innovations Of Black Youth, Tiera Tanksley

Journal of Computer Science Integration

This paper proposes a pedagogical extension of culturally responsive praxis called abolitionist, critical race pedagogy in CS. To showcase the power and potentiality of this pedagogy, this paper examines the experiences of 2 cohorts of Black high school students (n = 30) who participated in a critical race technology course that was taught during the dual pandemic of COVID-19 and anti-Black racism. The goal of this summer course was to employ an abolitionist, critical race pedagogy in CS to foster Black students’ ability to critically examine the ubiquity of anti-Black racism within the socio-technical architectures (e.g. code, data, algorithms and …

Culturally Responsive-Sustaining Computational Thinking: Enactment In Elementary Classrooms, Victoria Macann, Aman Yadav

Journal of Computer Science Integration

Technology has increasingly permeated many aspects of everyday life and this evolution raises the need for individuals to understand how the digital world works and what opportunities and risks it brings (Nouri, Zhang, Mannila & Norén, 2019). For this to be an experience for everyone, we need to rethink how we integrate computational thinking (CT) and provide teachers with tools to center their students’ identities, experiences, and cultures in the classroom. In this paper, we present two case studies of primary (elementary) teachers from a full primary (student ages 5–13) semi-rural school in the North Island of New Zealand that …

Cm-Ii Meditation As An Intervention To Reduce Stress And Improve Attention: A Study Of Ml Detection, Spectral Analysis, And Hrv Metrics, Sreekanth Gopi

Master of Science in Computer Science Theses

Students frequently face heightened stress due to academic and social pressures, particularly in de- manding fields like computer science and engineering. These challenges are often associated with serious mental health issues, including ADHD (Attention Deficit Hyperactivity Disorder), depression, and an increased risk of suicide. The average student attention span has notably decreased from 21⁄2 minutes to just 47 seconds, and now it typically takes about 25 minutes to switch attention to a new task (Mark, 2023). Research findings suggest that over 95% of individuals who die by suicide have been diagnosed with depression (Shahtahmasebi, 2013), and almost 20% of students …