Digital Commons Network^™

Machine Learning Model For Assessing Human Well-Being Using Brain Wave Activities, Sellappan Palaniappan, Rajasvaran Logeswaran, Yoke Leng Yong

Journal of Informatics and Web Engineering

This study presents a novel machine learning approach to assess human well-being through the analysis of brain wave activities. We developed a Random Forest classifier to categorize brain wave patterns into three states of well-being: good, normal, and bad. Using synthetic data simulating electroencephalography (EEG) readings, our model achieved an overall accuracy of 96.17%. The feature importance analysis revealed that alpha waves (34%) and beta waves (29%) were the most significant predictors of well-being states, which aligns with existing neuroscientific literature linking alpha activity to relaxation and beta activity to cognitive engagement. The confusion matrix demonstrated the model's particular strength …

A Conceptual Approach To Predicting Seismic Events And Flood Risks Using Convolutional Neural Networks, Mahmoud Rehan, Wan-Noorshahida Mohd-Isa, Noramiza Hashim

Journal of Informatics and Web Engineering

This paper explores the application of convolutional neural networks (CNNs) in predictive modelling for seismic events and flood risks, with a particular focus on forecasting extreme quantile events that exceed historical data limits. Traditional risk assessment methods often struggle to estimate such extremes, highlighting the need for more advanced predictive models capable of handling rare but high-impact events. This research enhances CNN architecture to improve accuracy in high quantile predictions by integrating multi-source spatiotemporal data, addressing a critical research gap. The methodology involves incorporating diverse datasets, including geospatial, meteorological, and historical seismic or flood records, into CNN models to augment …

Analysis Of Forensic Disk Imaging Tools For Data Acquisition And Preservation, Michelle Chee Ern Lim, Brandon Chen Hong Chow, Le Ying Lim, Tarini Shanbagamaran, Darren Yong Jun Lim, Ngu War Hlaing, Ahmad Sahban Rafsanjani

Journal of Informatics and Web Engineering

The identification, preservation, analysis, and presentation of electronic evidence to support legal or organizational inquiries constitute the discipline of digital forensics, which is crucial to contemporary investigations. A crucial component of forensic inquiry, disk imaging guarantees precision, dependability, and legal defensibility. To preserve the original evidence, disk imaging makes an identical, bit-by-bit duplicate of a digital storage device, capturing hidden data, deleted material, and active files. Given the critical role of disk imaging in forensic investigations, selecting the right tool is crucial for accuracy, efficiency, and compliance with forensic standards. This study assesses widely used tools, including AccessData FTK Imager, …

Performance Benchmarking: Pre-Trained Models And Custom Convolutional Neural Networks In Deep Learning, Sheemona Joseph C., S. Ganesh, S. Kannadhasan, K. Selvipriya

Journal of Informatics and Web Engineering

Recent advances in computer vision and deep learning, particularly Convolutional Neural Networks (CNNs), have significantly increased road safety. CNNs were used in this work to automatically detect and categorise traffic signs—a crucial task for autonomous vehicles (AVs) and advanced driver assistance systems (ADAS). These technologies' ability to accurately recognize traffic signs enables them to make informed decisions in real time, thereby elevating the standard for overall driving safety. The study uses a large, annotated dataset of images of traffic signs to train and assess the CNN model. We developed a model that can recognize a large number of traffic lights, …

The Impact Of Deep Learning In Brain Tumour Analysis, Sangeeta Giri, Manivel Kandasamy, Meet Rafaliya

Journal of Informatics and Web Engineering

The need for early and precise identification of abnormalities has made the detection and classification of brain tumours essential components of medical diagnosis. Because brain tumours are naturally complex and can have a wide range of sizes, shapes, and types, conventional diagnostic techniques like MRI interpretation and manual evaluations are difficult and time-consuming. Traditional methods frequently depend on human expertise, which is prone to errors, delays, and variability. Deep learning (DL) developments, on the other hand, have completely changed this field by providing increased automation, efficiency, and precision in tumour detection and classification because they can automatically extract pertinent features …

Exploring Big Data Management Approaches And Applications: A Case Study Of Real-Time Data Analytics In Air Traffic Management, Adeel Hashmi, Nouman Amjad, Muhammad Moiz Ullah Satti, Umar Hayat, Anam Mumtaz

Journal of Informatics and Web Engineering

The rapid proliferation of digital devices has generated vast amounts of data, presenting significant challenges in collection, processing, and analysis that traditional systems struggle to overcome. This study investigates big data management approaches, explicitly focusing on technologies capable of efficiently handling real-time data at scale. Within the context of Air Operations, we propose a Hadoop-based architecture designed to support the Observe-Orient-Decide-Act (OODA) loop and enhance air traffic management. By leveraging a distributed system deployed on a cloud-based platform, we demonstrate a cost-effective solution for optimised data processing and improved decision-making capabilities. Our analysis highlights the advantages of using Hadoop's distributed …

Editorial: Augmented Intelligence For Enabling Knowledge-Driven Decision Making, Heru Agus Santoso

Journal of Informatics and Web Engineering

More flexible and cooperative decision-making processes are required as a result of society's digital transformation. The role of augmented intelligence, that is the synergistic fusion of artificial intelligence and human judgment in facilitating knowledge-driven strategies across various domains is examined in this thematic issue. The integration of business intelligence and software engineering, which forms the foundation for creating intelligent, scalable, and explicable systems, is essential to this investigation. The six chosen papers in this issue show how machine learning techniques can be used to mine and model both structured (such as health records indicators) and unstructured (such as product reviews, …

K-Means Clustering Optimization Of Toddler Malnutrition Status Using Elbow Method, Femmi Widyawati, Ahmad Yahya Dawod, Heru Agus Santoso

Journal of Informatics and Web Engineering

The problem of nutritional status is still a major challenge in the health sector in developing countries, including Indonesia. Malnutrition in toddlers can have serious long-term impacts on children's growth and development, including increased risk of disease, impaired cognitive function, and low productivity in the future. To overcome this problem, an in-depth analysis is needed to determine the distribution of nutritional status of toddlers in one of the provincial capitals in Indonesia, which can be used as a basis for planning more effective interventions. This study uses the K-Means algorithm to classify areas based on the prevalence of malnutrition in …

Exploring The Relationship Between Academic Achievement And Chatgpt Usage: A Survey Of Higher Education Students In Malaysia, Mawar Madiah, A'Tiqah Rashidah Abu Samah

Journal of Informatics and Web Engineering

Students' academic achievement is caused by several factors which include cognitive capability, study habits, instructor effectiveness, family support, socio-economic status, and access to modern learning technologies like ChatGPT. Since its introduction, studies have suggested that ChatGPT has significantly impacted students’ academic performance. This study aims to explore whether students who perform well academically in higher education are more likely to use ChatGPT to enhance their studies and to gather their opinions on this learning tool. A survey was conducted with forty-one students with excellent academic performance. The results of this study show that most students with high academic achievement view …

Simplicial Decomposition And Realization, Matthew Ellison

Dartmouth College Ph.D Dissertations

In simplicial decomposition, we define two invariants --- V_Z and V_Q --- which represent notions of integral and rational volume of a certain class of simplicial complexes. We prove V_Z and V_Q are additive under disjoint union and connected sum, and investigate `integrality gaps' between the two quantities. We apply the theory to establish a conjecture of Sleator, Thurston, and Tarjan on tetrahedral fillings, and, as a corollary, obtain a new proof of Pournin's 2012 result on the diameter of the associahedron. In simplicial realization, we provide practical sufficient conditions and computer code to prove the existence of Euclidean embeddings …

Germanic Identity And Othering At The Inception Of Modernity: A Critical Translation Of The Geographic Addendum Of The Nuremberg Chronicle (1493), Nina Martin

University Honors Theses

The Nuremberg Chronicle (1493) is a late medieval humanist world chronicle compiled in its original Latin by Hartmann Schedel and translated into vernacular German by Georg Alt in the same year. It features exquisite woodcut illustrations from the studio of Michael Wohlgemut which bolstered the text's contemporaneous popularity among scholarly and lay audiences along with the unauthorized cheaper editions released by Johann Schönesperger within the decade. This project investigates depictions of collective identity as presented in the Geographic Addendum, a section of the endmatter discussing the peoples and lands of Europe which Schedel compiled from the writings of humanist scholar …

Integrating Iota Tangle And Artificial Intelligence (Ai) In Iot Network For Network Anomaly Detection, Saida Hafsa Rafique

Thesis/ Dissertation Defenses

The Internet of Things (IoT) ecosystem has advanced with the advent of Distributed Ledger Technology (DLT) and Artificial Intelligence (AI). Individually, DLT and AI have been explored for enhancement of data management, security, integrity and efficiency of IoT systems. In this thesis, the combined use to apply DLT and AI for network anomaly detection in IoT systems is considered. A framework is proposed to integrate IOTA Tangle, a DLT architecture with Machine Learning (ML)- Random Forest, Decision Trees, and LightGBM, to detect network anomalies in IoT systems. The proposed framework processes network traffic data from UNSW-NB15 dataset and categorizes it …

The Prevalence And Prediction Of Uncontrolled Type 2 Diabetes Mellitus In The Lower South Of Thailand, Akemat Wongpairin, Apiradee Lim, Haris Khurram

Journal of Health Research

Background: Type 2 diabetes (T2DM) is a major health issue globally, particularly in middle-income countries. This study assessed the prevalence and factors linked to uncontrolled T2DM and proposed a machine learning model for early prediction in southern Thailand.

Methods: Data from 60,903 T2DM patients (2016–2023) were obtained from Thailand's National Health Data Center. Multiple logistic regression identified associated factors, and machine learning models were compared for prediction.

Results: Uncontrolled T2DM prevalence was 53.5%, highest among patients ≤44 years (69.5%) and in Pattani (61.6%), Satun (58.1%), and Narathiwat (56.7%). Risk factors included being female, younger age, early or long disease duration, …

Predicting Hospitalisation In Older Patients Attended In The Emergency Department Using Classical And Artificial Intelligence Mathematical Strategies, Nere Larrea Aguirre, Susana García Gutiérrez, Oscar Miro, Javier Jacob, Pere Llorens, Guillermo Burillo-Putze, Cesáreo Fernández, Aitor Alquezar-Arbé, Sira Aguiló, Francisco Javier Montero Pérez, José J. Noceda Bermejo, María Teresa Maza Vera, Ángel García García, Patxi Ezponda, Juan González Del Castillo

Journal of Health Research

Background: The ageing population poses a significant challenge for health and social care systems. Emergency Departments (EDs) frequently experience overcrowding due to the high volume of patients and the limited availability of hospital beds. From the perspective of bed management planners, knowing the likelihood of a patient's admission at the earliest stage of care can be highly beneficial for effective resource planning. The goal of our study was to develop a prediction model to identify patients with a high probability of being admitted to the hospital.

Methods: We included all patients aged 65 or older who were treated over …

A Machine Learning-Driven Framework For Real Time Detection And Prevention Of Replica Node Attacks In Wireless Sensor Networks, Maram Pavani, Tanguturi Sharani, Amutha Jeevakumari S A

Northeast Journal of Complex Systems (NEJCS)

Mobile devices and wireless sensor networks (WSNs) are increasingly vulnerable to security threats such as unauthorized access and replica node attacks. Mobile devices face risks from replication and anomalous behavior, while attackers compromise WSNs by cloning legitimate nodes, thus threatening network integrity. Traditional security mechanisms often fall short in detecting such sophisticated threats, especially in resource-constrained environments. This research proposes a dual-component security system. A Machine Learning-Based Intrusion Detection System (IDS) for WSNs leverages Graph Neural Networks (GNNs) to detect replica nodes through structural network analysis and applies Federated Learning to preserve data privacy. The Sequential Probability Ratio Test (SPRT) …

Versatile Imidazole Scaffold With Potent Activity Against Multiple Apicomplexan Parasites, Monique Khim, Jemma Montgomery, Mariana Laureano De Souza, Melvin Delvillar, Lyssa J. Weible, Mayuri Prabakaran, Matthew A. Hulverson, Tyler Eck, Rammohan Y. Bheemanabonia, P. Holland Alday, David P. Rotella, J. Stone Doggett, Bart L. Staker, Kayode K. Ojo, Purnima Bhanot

Department of Chemistry and Biochemistry Faculty Scholarship and Creative Works

Malaria, toxoplasmosis, and cryptosporidiosis are caused by apicomplexan parasites Plasmodium spp., Toxoplasma gondii, and Cryptosporidium parvum, respectively, and pose major health challenges. Their therapies are inadequate, ineffective or threatened by drug resistance. The development of novel drugs against them requires innovative and resource-efficient strategies. We exploited the kinome conservation of these parasites to determine the cellular targets and effects of two Plasmodium falciparum inhibitors in T. gondii and C. parvum. The imidazoles, (R)-RY-1-165 and (R)-RY-1-185, were developed to target the cGMP dependent protein kinase of P. falciparum (PfPKG), orthologs of which are present in T. gondii and C. parvum. Using …

The Effects Of Race And Ethnicity On School Connectedness And Academic Outcomes Among Adolescents, Hafsa Khan Owens

College of Science and Health Theses and Dissertations

This study examines the role of school connectedness and how racial/ethnic identity in academic outcomes among adolescents/. Guided by the self-determination theory (Ryan & Deci, 2017) and Garcia-Coll et al’s (1996) Integrative Model, it was hypothesized that higher school connectedness predicts higher GPAs, that students whose racial/ethnic identity aligns with their schools’ majority composition experience stronger school connectedness, and the relationship between school connectedness and GPA will be stronger in schools with more matching peers. Participants included 138 youth (M = 14.98, SD = 1.92) from three urban schools (two K-8th; one high school). The sample was 33.3% Hispanic, 23.9% …

The Spectral Response Of Time-Resolved Piv In A Turbulent Boundary Layer, Peter Manovski, Wagih Abu Rowin, Henry Ng, Paul Gulotta, Matteo Giacobello, Charitha De Silva, Nicholas Hutchins, Ivan Marusic

Student Publications

This study presents the application of time-resolved particle image velocimetry (TR-PIV) to measure the mean and fluctuating velocity components in a turbulent boundary layer (TBL) over an axisymmetric body of revolution. A narrow wall-normal strip of the flow was captured using a synchronised high-speed laser and camera at a recording frequency of up to 80 kHz. The resulting streamwise and wall-normal velocity TR-PIV data were validated against hot-wire anemometry measurements and direct numerical simulations (DNS) of a flat plate under matched flow conditions. The mean flow results showed good agreement between all methods, while the expected attenuation due to the …

Collaboration Across The Silos: Consultation As A Means For Integrating Services For Twice-Exceptional Learners, Kimberly Bowers Yoshino

Electronic Theses and Dissertations

Twice-exceptional (2e) learners are underserved in American schools. As students who demonstrate both giftedness and disabilities, 2e learners maintain profiles that warrant tailored educational services across special and gifted education programs. Yet, these programs are considered institutionally mutually exclusive as legal mandates create de facto isolation — the antithesis of what is recommended for the students’ optimal development. School psychologists have been identified as professionals critically positioned to support 2e learners because of their specialized skill set, namely in consultation. However, little is known about their consultative practices with the educators who serve 2e learners or how they traverse school …

Multi-Label Classification Of Acoustic And Electronic Drum Sounds Using Machine Learning, Sean Perman

Electronic Theses and Dissertations

This paper presents a system for multi-class classification of drum sounds using audio signal processing and machine learning techniques. The project utilizes a diverse dataset of both acoustic and electronic drum samples and extracts ten distinct audio features to capture the timbral and temporal characteristics of each sound. The methodology includes signal preprocessing, feature extraction, and the application of supervised classification algorithms to distinguish between multiple drum classes. Experimental evaluations demonstrate that the selected features significantly enhance classification accuracy across a varied dataset. These findings underscore the effectiveness of combining traditional audio processing with modern machine learning, offering promising applications …

Enhancing Multi-Step Stock Price Forecasting With Social Media Sentiment And Engagement Metrics, Damilare Olaniyan

Electronic Theses and Dissertations

This thesis investigates whether social media sentiment can improve the accuracy of stock price prediction beyond traditional historical data. While financial markets have long relied on structured numerical indicators, the growing influence of public discourse on platforms like Twitter has introduced new opportunities for extracting market-relevant signals from unstructured text. The study focuses on four major technology firms and combines sentiment features derived from Twitter with historical stock prices in a hybrid machine learning framework. Engagement-weighted sentiment, linguistic complexity, and polarity intensity were extracted using natural language processing techniques and incorporated into classification and regression models. Results show that including …

Collaborative Federated Learning For Robots In Heterogeneous Environments, Karlan Schneider

Electronic Theses and Dissertations

This research investigates the performance of Federated Averaging (FedAvg) in simulated Federated Learning (FL) scenarios with varying degrees of environmental heterogeneity among robotic agents. The study explores the impact of data heterogeneity on both the convergence of FedAvg and the fairness of learning, with regard to consistency of performance across agents. Experiments were conducted with simulated robots trained to perform a target collection task, where a subset of agents encountered an unfamiliar environment. The results demonstrate that while FedAvg exhibits resilience to the introduction of new environmental data, it struggles to ensure both convergence and fairness in heterogeneous settings. Specifically, …

Photochemistry Of Quinones And Combustion-Derived Particles, Desiree J. Sarmiento

Electronic Theses and Dissertations

Quinones are ubiquitous species that can be produced from the photochemical aging of combustion-derived particles (CDPs). Polycyclic aromatic hydrocarbons (PAHs) are a major component of CDPs and are precursors to quinones and other oxidized products (OPAHs). My work first expanded on the PAH and OPAH photochemistry research of Dr. John Haynes, who showed that anthracene (ANT) oxidizes into 1,4-naphthoquinone (1,4-NAPQ), 1,4-anthraquinone (1,4-ANTQ), and 9,10-anthraquinone (9,10-ANTQ), and of Dr. Heather Runberg, who demonstrated the ability of ANT and these quinones to generate reactive oxygen species (ROS). Then at the Pacific Northwest National Laboratory (PNNL), I was given the opportunity to investigate …

Synthesis Of Anti-Schistosomal Heterocyclic Compounds Targeting Thioredoxin Glutathione Reductase, Alexander Stewart

Lawrence University Honors Projects

chistosomiasis is a deadly and debilitating parasitic disease caused by Schistosoma mansoni which affects 200+ million people annually who come into contact with contaminated water. Currently only one drug (Praziquantel) has been used to treat this disease for almost 50 years, and fear of resistance is growing, thus a new drug and new target is needed. Inhibition of the organism-specific redox defense protein Thioredoxin Glutathione Reductase (TGR) has lead to the death of the worm in vitro, indicating that it would be a good druggable target. Fragment based analysis and x-ray crystallography have returned the outline of a lead compound …

Study Of Agn Jet And Possible Connection Among Different Agn Classes, Shahjahan Iqbal

University Departments

No abstract provided.

Composite Magnetic Monopoles, Moreshwar Pathak

University Departments

No abstract provided.

Spin Precession In Magnetized Kerr Spacetime, Karthik Krishnamurthy Iyer

University Departments

No abstract provided.

Interacting Galaxy Clusters As A Probe To Cosmic Filaments, Shreya R. Kamath

University Departments

No abstract provided.

Photometric And Spectroscopic Properties Of Hydrogen-Rich Supernovae, Chaitrika B. M

University Departments

No abstract provided.

Image Recognition Algorithm For Pork Freshness Based On Yolov 8n, Wang Lian, Liu Jun, Pi Jie, Wang Daoying

Food and Machinery

［Objective］ To realize precise,swift,and non -invasive detection of pork freshness in large -scale cold meat industry chains based on computer vision technology.［Methods］ An image recognition algorithm for pork freshness is proposed based on YOLOv 8n.Various data augmentation methods are employed to enhance the pork feature extraction from images.The transfer learning experiment method is utilized,an appropriate optimizer is selected,and the training weights of the model are improved for higher accuracy in the final identification.Based on the YOLOv 8n image recognition algorithm,the improved YOLOv 8n-cls model is developed by data augmentation and optimizer improvement for the algorithm.［Results］ After transfer learning and improving …