Physical Sciences and Mathematics Commons^™

Developing A Predictive Model For Football Players’ Market Value Using Machine Learning, Muhammad Afif Jazimin Idris, Sew Lai Ng

Journal of Informatics and Web Engineering

Football is the world’s most popular sport, and evaluating the market value of players is crucial for clubs and managers in making informed decisions regarding transfers, contracts, and financial planning. This study aims to develop a predictive model to estimate the market value of football players using machine learning (ML) algorithms and real-life statistics performance data from the top five European leagues such as English Premier League, Italian Serie A, Spanish La Liga, German Bundesliga, and French Ligue 1 between the 2017/18 and 2019/20 seasons. By reviewing past research, various ML methods such as Random Forest, LightGBM, XGBoost, and Gradient …

Deep Learning Approaches To Autocorrelation Function And Signal-To-Noise Ratio Estimation In Noisy Images, Kai Liang Lew, Kok Swee Sim, Shing Chiang Tan

Journal of Informatics and Web Engineering

Accurate estimation of signal-to-noise ratio (SNR) in Scanning Electron Microscopy (SEM) is crucial because it evaluates the image quality. SEM images faced a challenge whereby Gaussian noise commonly appears in the images. Thus, researchers have developed several methods to estimate the SNR value. With the introduction of deep learning, most of the limitations in the classical methods can be addressed. This paper proposes a novel deep learning, CNN-based Calibration Map Network (CalibNet) to estimate the SNR value from SEM images using a calibration map between classical SNR and autocorrelation function SNR. The architecture consists of convolutional layers, rectified linear unit …

Loan Default Prediction Using Machine Learning Algorithms, Zhi Zheng Kang, Sin Yin Teh, Samuel Yong Guang Tan, Wei Chien Ng

Journal of Informatics and Web Engineering

Financial institutions constantly face at the risk of default by borrowers which can result in significant financial losses. It is essential to develop an appropriate predictive model for loan default to reduce these risks and minimise financial losses. The objective of this study is to identify the most suitable machine learning model to predict loan default by comparing four models which are Random Forest, Decision Tree, Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). Additionally, it also examines the key features influencing loan default prediction. The dataset used in this study is sourced from Kaggle and it consists …

Indonesian Language Sign Detection Using Mediapipe With Long Short-Term Memory (Lstm) Algorithm, Wargijono Utomo, Yogasetya Suhanda, Harun Ar-Rasyid, Andy Dharmalau

Journal of Informatics and Web Engineering

People with disabilities mostly communicate using sign language, but the public still has little understanding of the Indonesian Sign Language System (ISLS). This causes obstacles in daily interactions. Advances in artificial intelligence technology, especially artificial neural networks, open opportunities in sign language recognition, but are still in the development stage. This study aims to build a ISLS sign language recognition model using the LSTM approach and MediaPipe Hands. The method of collecting hand keypoint data, 25 sequences per gesture, and 36 alphabetic and numeric gestures. The dataset is divided into three categories, namely 80% training, 10% validation, and 10% testing. …

Mapping Relational Database To Full-Text Xml For Open Journal System Cross-Platform Article Distribution, Chee-Xiang Ling, Kok-Why Ng, Heru Agus Santoso

Journal of Informatics and Web Engineering

In academic publications, the automation of full-text eXtensible Markup Language (XML) is increasingly essential, as generating full-text XML for article distribution is a complex and time-consuming process that requires metadata extraction from a relational database and transformation into hierarchical structures such as Journal Article Tag Suite (JATS). The lack of automation in this transformation process may cause inconsistencies and inaccuracies and may cause errors due to human error. The primary aim is to develop an automation system for transforming metadata from a relational database to full-text XML by reducing errors and speeding the process of generating full-text XML. This is …

Exploring Generative Ai Recommender Systems In E-Commerce: Model, Evaluation Metric, And Comparative Review, Wan-Er Kong, Tong-Ern Tai, Palanichamy Naveen, Kok-Why Ng, Lucia Dwi Krisnawati

Journal of Informatics and Web Engineering

Generative Artificial Intelligence (GAI) is changing what can be done with Recommender Systems (RS) in e-commerce by allowing much more interactive, situationally aware, and highly tailored experiences for users. The purpose of this paper is to provide overall insight into how GAI, including Large Language Models (LLMs), Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and other emerging methods, is affecting the building and running of modern e-commerce RS. This paper classifies generative models into groups based on the type of models used, data modality, and specific domain of application. Their involvement in tasks such as personalized product ranking, content generation, …

User Behaviour Prediction In E-Commerce Using Logistic Regression, Wei-Wen Lee, Noramiza Hashim, Shaymaa Al-Juboori

Journal of Informatics and Web Engineering

From a psychological perspective, human behaviour reflects underlying thoughts and decision-making patterns, for example, consumer behaviour may correlate with the purchase decisions. In the fast-evolving e-commerce industry, predicting user behaviour is essential for enhancing marketing strategies, improving customer experiences, and increasing sales. However, traditional heuristic (e.g. market basket analysis) approaches to analyse buyer behaviour are often rigid and fail to adapt to complex consumer interactions. This research work develops a predictive model that analyses user behaviour based on data such as historical purchasing patterns and demographic attributes. Based on a review of previous studies, Logistic Regression (LR) is utilized as …

Identifying The Barriers To Digital Financial Inclusion In The Most Financially Excluded Country Using Machine Learning Algorithm, Yin Ting Chin, Hui Shan Lee

Journal of Informatics and Web Engineering

Despite the call of digital financial services (DFS) to improve inclusive growth and reduce poverty, the adoption of DFS remains low in Nigeria. The objective of this study is to examine the barriers of ability, access and usage of DFS in Nigeria. This study uses secondary data Global Findex year 2017 and year 2021 to predict the socioeconomic factors on the target variables of DFS (ability, access and usage). Using a machine learning (ML) algorithm, namely the J48 decision tree in the Waikato Environment for Knowledge Analysis (WEKA) software, this study analyses the predictive strength of variables such as gender, …

Robust Lane Detection Under Varying Lighting Conditions Using Adaptive Vision-Based Techniques, Habibatelrahman Mosaad Abdelfattah Mohamed Abdelaal, Tee Connie, Michael Kah Ong Goh

Journal of Informatics and Web Engineering

Reliable lane detection is crucial to autonomous driving but continues to be challenging with varying lighting conditions. Fluctuations in illuminations due to bright sunlight, shadows or low lighting at night can degrade the visual quality and adversely affect the accuracy of the lane detection results. This research proposes an adaptive approach for lane detection under different lighting scenarios. For daytime, a Region of Interest (ROI) masking and line averaging technique help in the stability and visibility of the lane markings. For nighttime conditions, a Probabilistic Hough Transform-based method improves lane detection in low-light environments. An evaluation tool has been developed …

Editorial: Ai-Enhanced Computing And Digital Transformation, Hairulnizam Mahdin

Journal of Informatics and Web Engineering

Artificial intelligence (AI), machine learning (ML), and advanced computing techniques are now central to the digital transformation journey, reshaping industries, academia, and society. This special issue of the Journal of Informatics and Web Engineering on AI-Enhanced Computing and Digital Transformation brings together contributions that reflect both technical innovation and societal applications. The featured articles span optimization methods, software quality improvements, data augmentation techniques, intelligent mobile applications, blockchain-based governance systems, and disaster management platforms. Together, they illustrate how computational advances not only strengthen efficiency and accuracy but also enable resilience in the face of global challenges. From a technical standpoint, metaheuristic …

Real-Time Read And Analysis Of Air Pollution Produced From Private Electrical Generators In Mosul City Using Lorawan, Mahmood Alfathe, Awfa Aladwani, Ali Abduljabbar

Journal of Informatics and Web Engineering

This study presents a novel, site-specific deployment of a Long Range Wide Area Network (LoRaWAN)-driven air pollution monitoring network specifically for the Iraqi city of Mosul, which is beset by widespread power outages and extensive utilization of decentralized diesel generators. While these generators mitigate electricity shortages, they are enormous contributors to urban air pollution, emitting high levels of CO2 and particulates. As opposed to previous studies, which concentrate on affluent urban areas, this research addresses a very deprived locale using an extensible low-power, low-cost LoRaWAN network and high-precision CO2 sensors (Sensirion SCD30 and MH-Z19) and The Things Network (TTN) for …

A Scoping Review Of Artificial Intelligence Research Trends In Mobile Applications, Asmau Usman, Ibrahim Anka Salihu, Abdulrahman Aminu Ghali, Bilkisu Larai Muhammad Bello, Aminu Aminu Mu'azu, Amina Sambo Magaji

Journal of Informatics and Web Engineering

Over the past decade, mobile devices have become an integral part of our daily routines, offering a broad spectrum of applications that enhance everyday tasks. As more people adopt smartphones, developers are increasingly focusing on improving app quality, particularly by incorporating artificial intelligence (AI) features. This growing trend has led to a surge of interest from both researchers and industry experts, who are aiming to explore AI integration in sectors such as healthcare, education, agriculture, and e-commerce. This study conducts a thorough review of AI applications on mobile platforms by analysing 98 scholarly articles published between 2014 and 2024 from …

Impact Of Data Quality Types On Computational Time In Data Source Selection Using Ant Colony Optimization, Nor Amalina Mohd Sabri, Abd Samad Hasan Basari, Nurul Akmar Emran

Journal of Informatics and Web Engineering

Data quality varies dramatically from source to source, even within the same domain. Given these challenges, data source selection has emerged as a crucial step in information integration. It demands efficient and scalable approaches that can handle massive data volumes while ensuring the quality of results. Adapting the ACO algorithm to solve the data sources selection problems may lead to inconsistent computational time if the data sources provided are vary in quality. These challenges bring the issues of time consuming in selecting the required data sources. However, how much the computational time needed in solving the data sources selection is …

Lightweight String Similarity Approaches For Duplicate Detection In Academic Titles, Fahrudin Mukti Wibowo, Muhammad Zidny Nafan, Muhamad Azrino Gustalika, Harinda Fernando, Muhammad Hussain, Nur Afiqah Sahadun

Journal of Informatics and Web Engineering

This study addresses the critical challenge of detecting duplicate final year project (FYP) titles in academic institutions, where minor variations like reordering, synonyms, and paraphrasing often obscure plagiarism. We systematically evaluate four string similarity algorithms - Jaro-Winkler, Levenshtein Edit Distance, TF-IDF with Cosine Similarity, and Jaccard Similarity - using a synthetic dataset of 250 title pairs representing common duplication patterns. Our experiments reveal that character-based methods (Jaro-Winkler and Edit Distance) achieve perfect detection (F1-score=1.0) for literal matches, including typographical variations and phrase reordering. At the same time, TF-IDF demonstrates strong semantic capability (F1-score=0.95), albeit with some false positives. Jaccard Similarity …

Improving Code Effectiveness Through Refactoring: A Case Study, Abdullah Almogahed, Manal Othman, Mazni Omar, Samera Obaid Barraood, Abdul Rehman Gilal

Journal of Informatics and Web Engineering

Software refactoring is a crucial practice in modern software development methodologies, such as Agile and DevOps, as it enables teams to iteratively improve and evolve their codebases while minimizing the risk of introducing bugs or regressions. It fosters a culture of continuous improvement and code hygiene, ultimately leading to more robust, maintainable, and scalable software systems. However, research examining the impact of refactoring on code effectiveness is scarce. This study, therefore, seeks to investigate the impact of refactoring methods on the code’s effectiveness. The study was carried out in four distinct phases: refactoring methods selection, case study selection, software metric …

Societies’ Funds Management System Using Blockchain, Jun Xiang Lau, Nur Ziadah Harun, Suziyanti Marjudi, Abd Samad Hasan Basari, Nur Amlya Abd Majid, Roziyani Setik

Journal of Informatics and Web Engineering

Lack of transparency of the funds and lack of immutability of the funds’ records are large problems today, especially in the societies’ funds. Leveraging the Ethereum Blockchain, the system ensures complete transparency and security in recording and accessing financial transactions for any society. Advanced encryption and blockchain consensus protocols guarantee data privacy and resilience against fraud or tampering. The information entered in the system helps the treasurer to effectively manage funds and accurately and transparently pass appropriate financial transactions. This project meets the challenges of transparency and immutability in society’s fund records, providing an assurance system and promoting growth for …

Implementation Of Conjugate Gradient Method For Estimating Inflation Rate In Malaysia, Shin Yi Wong, Nur Syarafina Mohamed, Norhaslinda Zullpakkal

Journal of Informatics and Web Engineering

Optimization methods are valuable for making decisions and identifying the most suitable alternative based on a given objective function. One of the mathematical optimization methods is Conjugate Gradient (CG) method which is commonly used to solve large-scale unconstrained optimization systems with less storage space. Recently, various optimization methods have been studied and used in economics estimating. However, just a few studies have predicted inflation rate using modified CG method. Random initial points are tested on New Three-Terms (NTT) which are modified Rivaie-Mustafa-Ismail-Leong (RMIL+) and Umar Mustapha Waziri (UMW) CG method with ten optimization test functions suggested by Andrei using MATLAB. …

Conjugate Gradient Methods In Fitting Precipitation Of Rainfall Data In Malaysia, Hua Ru Tang, Nur Syarafina Mohamed, Nurul Hajar Mohd Yusoff, Norhaslinda Zullpakkal

Journal of Informatics and Web Engineering

Conjugate gradient method (CGM) is one of the most efficient numerical methods for solving unconstrained optimization problems. It is also known as an iterative method with simple formulation. The classical CGM has always been an interest to the current researchers in improving the formulation which are categorized into three-term (TTCGM), spectral (SCGM), hybrid and scaled CGM. Although there are many variations of the CGM available, choosing the most efficient and effective one for a particular problem can be a time-consuming process. In this study, spectral Hestenes-Stiefel (sHS) CGM with the greatest NOI and central processing time per unit (CPU time) …

Enhancing Imbalanced Data Augmentation: A Comparative Study Of Ganified-Smote And Latent Factor Integration, Rusma Anieza Ruslan, Nureize Arbaiy, Pei-Chun Lin

Journal of Informatics and Web Engineering

One such serious problem in machine learning (ML) is imbalanced datasets. Minority class samples are usually sparse but hold significant meaning. The model can become biased toward the majority class due to unbalanced class distribution. This results in fraudulently high accuracy without being able to detect minority cases. This bias is also most perilous in critical applications, where ignoring minority cases can be highly destructive. To overcome this problem, the Synthetic Minority Oversampling Technique (SMOTE) is one of the most widely used. SMOTE creates balanced class distribution by interpolating between existing minority samples. It creates samples that are too close …

Re: Comments On The Draft Final Butte Mine Waste Repository Geotechnical Investigation Work Plan (Dated September 23, 2025), Emma Rott

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Needs Analysis Of Interactive Learning Media For Teachingheat Transfer In Physics: Students’ And Teachers’Perspectives, Rizki Eka Shintya, Ika Mustika Sari, Ida Kaniawati

Jurnal Pendidikan Sains

Heat transfer material in physics is known to be abstract. This study specifically analyzes the conceptual difficulties, and visualization needs specific to this material, which is different from the analysis of physics needs in general. This analysis does not only look for the need for traditional media but explicitly looks for the need for interactive media (simulation, animation, virtual laboratory). Therefore, this study aims to identify the need for interactive learning media in physics education, specifically in the teaching of heat transfer, from the perspective of students and teachers in West Java, Indonesia. Using a quantitative descriptive survey, data were …

Efficacy Of Teaching At The Right Level Approach Combined With The Problem-Based Learning Model On Science Learning Outcomes At Middle School, Heri Syahputra Azhar Panjaitan, Ryan Dwi Puspita

Jurnal Pendidikan Sains

This research seeks to evaluate the efficacy of the Teaching at the Right Level (TaRL) strategy in conjunction with the Problem-Based Learning (PBL) model on the scientific learning outcomes of eighth-grade students at middle school Islam Terpadu Khairul Imam. This research addresses the inadequate scientific problem-solving abilities and science learning outcomes of Indonesian students, necessitating a more interactive and individualized instructional technique. The methodology used is a quasi-experimental design with a nonequivalent control group. The sample included two purposively chosen eighth-grade classes: one experimental class (n=25) underwent the TaRL–PBL intervention, whereas the control class (n=25) simply used the lecture approach. …

Tableau Part Ii - October 2025, Rubab Shahzad

Day Family Research Lab Workshop Series

Part Two of Introduction to Tableau. Learn to make cool visualizations using Tableau. A hands-on opportunity where we will go over calculated fields, hierarchies, unions, dashboards, and stories.

Prior experience with Tableau is recommended

Skin Cancer Image Classification Using Deep Learning With Data Segmentation Technique, Akhilesh Kumar Shrivas, Hema Vastrakar

Karbala International Journal of Modern Science

The human skin is an impressive organ and structural element often impacted by a diverse range of recognized and unknown diseases. Diagnosing disorders that affect the outermost layer of the body is the most uncertain and difficult component in the scientific field. Dermatological diseases are one of the most significant health concerns in the 21st century since their identification is challenging and costly, plagued with challenges and the subjectivity that comes with human interpretation. The main objective of this piece of research work is to develop a robust model for the classification of skin cancer diseases using deep convolution neural …

Arizona’S Experiential Learning Opportunities: Regional Security Operations Centers And Cybersecurity Clinics, Joshua Kipers, Paul Wagner, Robert J. Honomichl

Journal of Cybersecurity Education, Research and Practice

The increasing frequency, sophistication, and economic impact of cybersecurity incidents have intensified the global demand for a skilled cybersecurity workforce. Traditional academic programs often fail to provide the applied experience necessary to prepare graduates for the rapidly evolving threat landscape. This paper examines Arizona’s innovative approaches to experiential cybersecurity education through the establishment of Regional Security Operations Centers (RSOCs) and the Arizona Cybersecurity Clinic. These initiatives integrate Kolb’s Experiential Learning Theory and the NICE Cybersecurity Workforce Framework to align academic preparation with real-world practice. The RSOCs, supported by the Arizona Department of Homeland Security, provide paid student internships focused on …

Second Moment Of Degree Three L-Functions, Sampurna Pal

Doctoral Theses

Let $F$ be a Hecke-Maa\ss\  cusp form for $SL(3,\mathbb{Z})$. In this dissertation, we obtain a non-trivial upper bound of the second moment of $L(F,s)$ in the $t$-aspect: $$ \int_{T}^{2T}|L(F,1/2+it)|^2 dt\ll_{F,\epsilon} T^{3/2-3/32+\epsilon}.$$ Immediate corollaries include improvements over the existing results on the Subconvexity bound for self-dual $GL(3)$ $L$-functions in the $t$-aspect and for self-dual $GL(3)\times GL(2)$ $L$-functions in the $GL(2)$ spectral aspect, the error term in the Rankin-Selberg problem, and the zero density estimate for $GL(3)$ $L$-functions.

An Integrated Pcb-Based Heating And Auto-Ranging Platform For Volatile Organic Compound (Voc) Detection Using Carbon Nanotube Based Sensors, Thomas Kalach

USF Tampa Graduate Theses and Dissertations

This thesis presents the development, characterization, and integration of a novel low-cost, high-dynamic-range sensor platform for the detection of volatile organic compounds (VOCs), leveraging the unique electrical properties of carbon nanotube (CNT) thin films. The platform introduces a fully integrated auto-ranging analog front-end circuit capable of real-time resistance measurement spanning over eight orders of magnitude ranging from tens of ohms to hundreds of megaohms, without compromising signal resolution or precision. This was achieved through a digitally controlled, multi-path feedback architecture and controllable current source.

To further enhance sensor performance, the system incorporates a copper trace heater beneath the sensor array, …

Development Of Natural Product Analogs As Therapeutic Agents, Jacob Mara

Theses and Dissertations

Cancer stem cells (CSC) are cancer cells in a tumor mass that may self-renew and develop into various cell types, resulting in a heterogeneous tumor. CSCs have been shown to have a role in many facets of cancer formation, including tumor genesis, proliferation, and metastatic activity. They are also implicated in chemotherapeutic treatment resistance and the recurrence of some malignancies. Based on these capacities, CSCs have been identified as the next target for cancer therapy and control. Salinomycin's potential for therapeutic use based on ongoing research, as well as the synthesis of its derivatives and their biological activity, will also …

10.13.2025 Ored Connect, Liz Williamson

ORED Newsletter

• ORED Small Grants Program RFP
• Mississippi Impact Grants RFP
• Contest to Promote Lab Practices
• Field Fest
• AAAS Membership for Faculty, Staff, and Students 

Draft Final 2024 Residential Metals Abatement Program (Rmap) Baptist Student Union (Former Mckinley School) Soil Remedial Action Construction Completion Report, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.