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Cultural Adaptation And Psychometric Characteristics Of The Child Oral And Motor Proficiency Scale (Chomps)-Turkish Version, Ebru Umay, Si̇bel Eyi̇gör, Damla Cankurtaran, Sema Kalkan, Ni̇hal Tezel, Cuma Uz, Şükran Güzel, Fatma Balli Uz, Güler Gözpinar, Recep Gayir, Fatma Nazli, Keri̇m Demi̇rsöz, Ece Ünlü Akyüz, Britt Pados

Turkish Journal of Medical Sciences

Background/aim: The child oral and motor proficiency scale (ChOMPS) is a parent-reported assessment that evaluates a child’s eating and drinking skills within the framework of all related motor functions. It has been found to be useful in research studies and recommended in reviews. The purpose of this study was to perform the translation and cultural adaptation of the English version of the ChOMPS to Turkish and conduct psychometric testing of the ChOMPS–Turkish version, including reliability and validity.

Materials and methods: This study was conducted with 185 children. Cronbach’s α, corrected item-to-total correlations, coefficient of variation, and Cronbach’s α when one …

The Importance Of Galectin-3 In The Diagnosis Of Childhood Acute Appendicitis ​, Onur Yalçin, Aybegüm Kalyoncu Ayçenk, Ali̇ Aygün, Ahmet Burak Günpinar, Mehmet Seyfetti̇n Saribaş, Volkan Altinok, Tevfi̇k Noyan

Turkish Journal of Medical Sciences

Background/aim: Acute appendicitis (AA) is a frequent indication for emergency surgery in children; accurate diagnosis is essential to prevent complications. We examined whether serum Galectin-3 is associated with pediatric AA and quantified its diagnostic performance relative to routine inflammatory markers.

Materials and methods: This prospective, single-center study was conducted in a tertiary emergency department between July 1, 2023, and January 31, 2024. Children younger than 18 years with pathologically confirmed AA were enrolled as cases; age-compatible healthy volunteers served as controls. Venous blood was obtained at presentation before any therapeutic intervention. Galectin-3 concentrations were quantified using a commercial enzyme-linked immunosorbent …

Differential Patterns Of Gut And Oral Microbiomes In Hispanic Individuals With Cognitive Impairment, Yannick N. Wadop, Erin L. Vasquez, Julia J. Mathews, Jazmyn A. S. Muhammad, Rosa P. Mavarez, Claudia Satizabal, Mitzi M. Gonzales, Jeremy A. Tanner, Gladys E. Maestre, Bernard Fongang

School of Medicine Publications

Alterations in both oral and gut microbiomes have been associated with Alzheimer's disease and related dementia (ADRD). While extensive research has focused on the role of gut dysbiosis in ADRD, the contribution of the oral microbiome remains relatively understudied. This study aims to evaluate distinct patterns and potential synergistic effects of oral and gut microbiomes in a cohort of predominantly Hispanic individuals with cognitive impairment (CI) and without cognitive impairment (NC). We conducted 16S rRNA gene sequencing on stool and saliva samples from 32 participants (17 CI, 15 NC; 62.5% female, mean age = 70.4 ± 6.2 years) recruited in …

Zero Trust - The Future Of Cybersecurity, Danial Haider, Shougfta Mushtaq, Hasnat Ali, Mazliham Mohd Su’Ud

Journal of Informatics and Web Engineering

The recent Zero-Trust Architecture (ZTA) is progressively adopted to the develop network security by assuming no implicit trust within or outside an organization’s boundary. Though, ZTA faces substantial challenges in detecting sophisticated and developing cyber threats, particularly due to its trust on traditional security mechanisms that struggle to manage internal threats and sophisticated attack techniques. To report these shortcomings, the proposed study discovers the combination of advanced machine learning (ML) and deep learning (DL) performances to improve the anomaly detection proficiencies within ZTA environments. The study develops the CICIDS2017 dataset, which contains diverse and realistic network traffic patterns, to assess …

Training The Brain: A Machine Learning Approach To Predicting Wellbeing Through Intentional Thought Pattern Modification, Sellappan Palaniappan, Rajasvaran Logeswaran, Kasthuri Subaramaniam, Oras Baker, Bui Ngoc Dung

Journal of Informatics and Web Engineering

This study provides a quantitative framework for wellbeing outcome prediction through intentional cognitive pattern alteration. We demonstrated 81.67% accurate prediction of wellbeing states, in a three-level classification (Low, Medium, High), using a Random Forest classifier with 16 features from psychological, physiological, and behavioural metrics. Our model singles out the gratitude cultivation (21.3%) and peace duration (23.7%) as the strongest predictors of positive well-being outcomes, which provides empirical support to traditional approaches of cognitive training with empirical evidence. Analysis of 1,000 synthetic cases shows that consistent practice of positive thought patterns over 3-6 months can strongly shift wellbeing states, with key …

Implementation Of Lightweight Machine Learning Models For Real-Time Text Classification On Resource-Constrained Devices, Marwah Zaid Mohammed Al-Helali, Naveen Palanichamy, K. Revathi

Journal of Informatics and Web Engineering

This paper addresses the growing need for implementing intelligent Natural Language Processing (NLP) systems on low-power, memory-limited devices such as Raspberry Pi, mobile phones, and IoT edge hardware. As edge computing and smart devices proliferate, there is an urgent need for more advanced NLP technology that does not require constant cloud access and is efficient in computing and provides results in real time. While deep learning and cloud-based models typically offer high text-classification accuracy and have demonstrated exceptional performance across a range of NLP tasks, they are often too resource-intensive for real-time deployment in constrained environments. To overcome these limitations, …

Review On Secure And Efficient Iot-Based Healthcare System With The Integration Of Machine Learning And Firewalls, Muhammad Awais, Syeda Samar Fatima, Jawaid Iqbal

Journal of Informatics and Web Engineering

The integration of the Internet of Things (IoT) into healthcare would mean a revolutionized approach in patient monitoring, diagnosis, and treatment, making this quite some development in healthcare delivery. This review has focused on how the integration of IoT with Machine Learning (ML) and stringent security measures tackle the challenging situation of data privacy and cyber threats in healthcare. Current methodologies point toward how essential advanced sensors, cloud computing, and wireless technologies for IoT-based healthcare systems necessary to secure patient data. patient record kept in files and now forward to the cloud database system so that in any case of …

Addressing Iot Security Challenges Through Advanced Machine Learning And Encryption, Ahmad Aziem Khushairi Anuar, Ahmad Anwar Zainuddin, Ahmad Adlan Abdul Halim, Dek Rina

Journal of Informatics and Web Engineering

The rapid growth of Internet of Things (IoT) devices, including smartwatches, home assistants, and connected appliances, has brought significant convenience to daily life, but it has also introduced serious security challenges. These devices often transmit sensitive data, making them vulnerable to theft, misuse, and unauthorized access. Current security measures are insufficient to address the complex and evolving nature of IoT systems, leaving many of them exposed to potential breaches and cyberattacks. This review explores recent developments in IoT security, focusing on how advanced technologies, such as machine learning, can be utilized to enhance the protection of IoT systems. The main …

Research Article Recommender Systems: A Comprehensive Review Of Models, Approaches And Evaluation Metrics, Sir-Yuean Lim, Noramiza Hashim, Lanh Le Thanh

Journal of Informatics and Web Engineering

With the advent of the current digital era, individuals across the developed world are commonly equipped with devices that can access vast amounts of information at their fingertips. What was considered an impossible feat was realized through remarkable technological advancements. This positive transformation has had a profound impact on education, where traditional knowledge management, such as libraries, are no longer a primary determinant of a student’s academic success. Instead, it has been replaced by the internet as a medium for learning, practicing, and topic exploration. However, the sheer volume of the ever-increasing information available online can easily overwhelm a user, …

Optimised Heterogeneous Handover In Mobile Ipv6 Using Enhanced Predictive Fast Proxy With Media Independent Handover (Mih) Support, Aliyu Aminu Abdulhadi, Nura Mukhtar

Journal of Informatics and Web Engineering

In wireless networks, handover performance is essential for enabling real-time traffic applications. Long handover delays make it impossible for a Mobile Node (MN) to send and receive data packets, which is very undesirable for real-time applications like video conferencing and VoIP. Therefore, in order to guarantee better handover performance, decreasing handover duration is crucial. The Internet Engineering Task Force (IETF) has standardized Fast Proxy Mobile IPv6 (FPMIPv6) as an enhancement to the novel Proxy Mobile IPv6 (PMIPv6) to attain better handover performance. FPMIPv6 functions in two modes: predictive and reactive, using a link-layer triggering mechanism. The predictive mode uses link-layer …

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 …

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. …

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, …

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 …

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 …

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.

Quantifying Interest In And Sentiment Of Online Media About Greenhouse Gas Emissions From Cattle Production In The United States, Michael L. Smith, Jinho Jung, Nicole Olynk Widmar, Danielle J. Ufer, Maria Berikou, Jayson Lusk

Department of Agricultural Economics Faculty Publications

The public is paying increased attention to the environmental impacts associated with cattle production systems in the US. Thus, cattle producers are increasingly exploring ways to communicate their efforts to reduce those environmental and climate impacts. This study uses online and social media data from 2018 to 2022 to examine public interest in and sentiment about beef and dairy cattle production when discussed within the context of greenhouse gas (GHG) emissions. Variation in mentions and net sentiment over the reporting period is quantified and analyzed. Over the period of data collection, which was interrupted by the COVID-19 pandemic, we find …

Barriers To Machine Learning Adoption In Regulated Electric Utilities, Donald R. Shiflet Jr.

USF Tampa Graduate Theses and Dissertations

Machine learning (ML) technologies have the potential to revolutionize regulated electric utilities by improving operational efficiency, enabling predictive maintenance, and optimizing energy management. Despite these advantages, the adoption of ML in this sector lags other industries due to technical, organizational, and regulatory barriers. This research, grounded in the Technology-Organization-Environment (TOE) framework, explores these barriers to uncover actionable solutions for integration. The study identifies key challenges, including explainability, cybersecurity, workforce resistance, and regulatory ambiguity to ML adoption in electric utilities. Utilizing an exploratory qualitative methodology, this approach integrates insights from the literature and industry interviews to rank barriers by frequency, severity, …

Engaged Climate Change Pedagogy: Lessons From 15 Years Of Interdisciplinary Climate Change Education, J. Alejandro Artiga-Purcell, Costanza Rampini, Anne Marie Todd, Eugene C. Cordero

Faculty Research, Scholarly, and Creative Activity

This article explores lessons learned from 15 years of an interdisciplinary, team-taught course on global climate change at a minority-serving public university in the United States San Francisco Bay Area integrating climate science with policymaking, public communication strategies, and principles of climate justice. First taught in 2007, 16 faculty from 5 different departments collaborated across disciplines to produce innovative teaching practices and curriculum design that evolved through each iteration of the course. Theme analysis of two roundtable discussions with course instructors identified three themes: changing course context, integrative learning, and community engagement. The essay concludes with lessons learned from this …

Eutrophication, Hope Northagen, Sarah Erwin, Ethan Gilliam

All Current Publications

Eutrophication refers to the ecological state of a water body after excessive nutrient pollution. The external introduction of nutrients (primarily nitrogen and phosphorus) accelerates the growth and reproduction of plants, algae, and microbes. This fact sheet explains where eutrophication comes from, its impacts, how it is treated, and what you can do to help restore and protect our lakes, rivers, and oceans.

Generative Modeling For Interpretable Anomaly Detection In Medical Imaging: Applications In Failure Detection And Data Curation, Mckell E Woodland, Mais Altaie, Caleb S O'Connor, Austin H Castelo, Olubunmi C Lebimoyo, Aashish C Gupta, Joshua P Yung, Paul E Kinahan, Clifton D Fuller, Eugene J Koay, Bruno C Odisio, Ankit B Patel, Kristy K Brock

Faculty, Staff and Students Publications

This work aims to leverage generative modeling-based anomaly detection to enhance interpretability in AI failure detection systems and to aid data curation for large repositories. For failure detection interpretability, this retrospective study utilized 3339 CT scans (525 patients), divided patient-wise into training, baseline test, and anomaly (having failure-causing attributes—e.g., needles, ascites) test datasets. For data curation, 112,120 ChestX-ray14 radiographs were used for training and 2036 radiographs from the Medical Imaging and Data Resource Center for testing, categorized as baseline or anomalous based on attribute alignment with ChestX-ray14. StyleGAN2 networks modeled the training distributions. Test images were reconstructed with backpropagation and …

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 …