Engineering Commons^™

Crisis Observatory: Extracting Credible Signals During A Crisis In The Age Of Llms, Kuan-Chieh Lo, Pranav Maneriker, Sriram Sai Ganesh, Dominik Winecki, Kelly Garrett, Ayaz Hyder, Arnab Nandi, Valerie Shalin, Shannon A. Bowen Ph.D., Amit Sheth, Srinivasan Parthasarathy

Publications

Systems for crisis response have required several different models for the analysis of unstructured text, such as identifying needs, locations, topics, routing, and matching of needs with available responders. Large Language Models (LLMs) have replaced task-specific models across various language processing tasks. However, LLMs are known to be limited by their training data, collected before the crisis. In this demo, we explore the use of LLMs for crisis response scenarios with rapidly evolving information environments. We show how the augmentation of these models with external reliable sources of crisis-specific information can help build adaptive systems for response. The demonstration video …

Mosquito Classification And Explainability From Image Data Via Deep Learning Techniques, Farhat Binte Azam

USF Tampa Graduate Theses and Dissertations

According to the World Health Organization (WHO), mosquitoes are the deadliest animals on Earth, responsible for more human deaths annually than any other species. Mosquito-borne illnesses continue to pose severe risks to global health. In 2015 alone, there were an estimated 214 million malaria cases worldwide. Similarly, a 2016 report from the Centers for Disease Control and Prevention (CDC) revealed that Puerto Rico’s Department of Health received over 62,500 suspected cases of Zika, with 29,345 confirmed positive cases. In 2019, Southeast Asia experienced its worst dengue outbreak in recorded history. Of the approximately 4,500 mosquito species distributed across 34 genera, …

Computational Investigation Of The Role Of 3d Genome Architecture In The Lifecycle Of The Malaria Parasite, Srish Maulik

College of Engineering Summer Undergraduate Research Program

Malaria, a mosquito-borne infectious disease caused by the Plasmodium parasite, is responsible for more than a half a million deaths per year, the vast majority of which occur in central Africa. The parasite undergoes an incredibly complex cell molecular transformation as it transitions from living in mosquitoes to living in humans with different sets of genes being activated or silenced in order to evade the immune system of the host. Understanding how its genome guides this transition is critical for developing adequate treatments. In this project, we aim to develop a computational framework for investigating the role of the three …

Ontology Features-Based Arabic Text Augmentation Using Word2vec, Enas Tariq Khudair, Onsa Lazzez, Mourad Zaied, Tarek M. Hamdani, Ahmed T. Sadiq, Habib Chabchoub, Adel M. Alimi

Iraqi Journal for Computer Science and Mathematics

Text augmentation plays a major role when data is scarce. In this context, there are few Arabic news texts for specific purposes, and hence, there is a dire need to generate Arabic text, especially news. This paper presents an enhanced approach to Arabic text augmentation based on Arabic ontology features. The Arabic part of speech, particularly adjectives, verbs, and prepositions, and the ontology properties regarding such parts to create new texts, make up the first stage of the system, which has multiple stages. Word2Vector (Word2Vec) plays a pivotal role in giving Arabic ontology features to the specific Arabic Part of …

The Utilisation Of The Fourth Industrial Revolution (4ir) Technologies In E-Government Service Delivery: A Systematic Literature Review, Arnet Zitha, Noluntu Mpekoa, Sheethal Tom

African Conference on Information Systems and Technology

The integration of Fourth Industrial Revolution (4IR) technologies is transforming e-Government by boosting citizen engagement and enhancing service efficiency. However, gaps still exist in understanding the various applications, impacts, and barriers to adoption. This systematic review synthesises literature from 16 studies published between 2017 and 2025, illustrating how technologies like blockchain, artificial intelligence, big data, Internet of Things, and machine learning are employed and their effects on e-Government service delivery. The review reveals that 4IR technologies continue to play a vital role in e-Government services by addressing security threats, simplifying verification and authentication, building trust, and improving the quality and …

Smart Mobility Technologies In Urban Areas Of Emerging Economies: A Bibliometric Analysis, Peter Mugisha, Rose Luke, Joash Mageto, Hossana Twinomurinzi

African Conference on Information Systems and Technology

Despite the adoption of smart mobility solutions in emerging economies, challenges such as traffic congestion, pollution and inadequate infrastructure still persist. This study analyses 540 scholarly articles published between 2003 and 2024 to evaluate how smart mobility technologies – such as Intelligent Transportation Systems (ITS), Internet of Things (IoT) and Artificial Intelligence (AI) – have been implemented in these regions. Data was retrieved from Scopus and Web of Science and analysed using Biblioshiny for bibliometric mapping and Atlas.ti for thematic analysis. The review identifies research trends and gaps, showing how ITS has improved transport management in cities like Nairobi, and …

Effects Of Lossy Compression Data On Machine Learning Models, Max H. Faykus Iii

All Dissertations

Machine learning is a fundamental tool that is incorporated in every field across academia and other industries. Due to the large amount of data needed for training machine learning models, lossy compression plays a crucial role in storing data. Machine learning involves the use of algorithms and models to learn patterns in data. This allows the AI to make decisions without specific programming. On the other hand, compression utilizes encoding and decoding techniques to reduce the size of files. Compression is either lossy or lossless, lossy causes a loss of data while lossless preserves the data. This dissertation will explore …

Understanding Russia’S Cyber Policies, Strategies, And Doctrines, Bryan Hancock, Hanh Nguyen, Olga Karpoyan, Ekzhin Ear, Shouhuai Xu

Military Cyber Affairs

This study analyzes the strengths and weaknesses of Russia’s cyber policies, strategies, and doctrines through a systematic set of attributes, leading to key insights: (i) Russia has proactively adapted its cyber policies, strategies, and doctrines to its evolving environment; (ii) Russia actively conducts cognitive warfare, but remains equally vulnerable to it; and (iii) Russia’s cyber posture faces significant challenges, including a limited technological base, shortage of skilled personnel, and restrictive approach to information control, all of which undermine the effectiveness of its strategies. These insights offer valuable implications for US Cyber Command and the Department of Defense.

Portuguese For Mozambican Mothers – A Language Empowerment App, Josh Kindarara, Alexandre Lopes, Gwendolyn Patwardhan, Riana Santos, Sara Wheeler

Computer Science and Engineering Senior Theses

In Mecúfi, Mozambique, the mothers of children who attend school through the DIFF EDUCATION program have a strong desire to learn Portuguese, the country’s national language, due to colonization. However, they have minimal formal education and little to no experience with technology. They are unable to read or write in any language, including their native language, Makhuwa, making many current educational resources unusable. This leaves these mothers without accessible, culturally relevant tools to help address their linguistic needs.

This project focuses on developing an educational application designed for non-literate mothers in rural Mozambique. The app emphasizes visual and auditory learning …

Meta-Learning Hyperparameters For Foundation Model Adaptation In Remote-Sensing Imagery, Zichen Tian, Yaoyao Liu, Qianru Sun

Research Collection School Of Computing and Information Systems

Training large foundation models of remote-sensing (RS) images is almost impossible due to the limited and long-tailed data problems. Fine-tuning natural image pre-trained models on RS images is a straightforward solution. To reduce computational costs and improve performance on tail classes, existing methods apply parameter-efficient fine-tuning (PEFT) techniques, such as LoRA and AdaptFormer. However, we observe that fixed hyperparameters -- such as intra-layer positions, layer depth, and scaling factors, can considerably hinder PEFT performance, as fine-tuning on RS images proves highly sensitive to these settings. To address this, we propose MetaPEFT, a method incorporating adaptive scalers that dynamically adjust module …

Frontlines Of Influence: State Vs. Nonstate Disinformation Campaigns, Lily Wershbale

Cybersecurity Undergraduate Research Showcase

This paper examines the evolution of disinformation campaigns conducted by state and nonstate actors, focusing specifically on Russia and the Islamic State as representative case studies. Through historical examples and qualitative comparative analysis, this research identifies the similarities and differences in disinformation’s role in actors’ core missions, resource allocation, and targeting decisions. This paper further explores the implications of artificial intelligence on disinformation campaigns, investigating how emerging technology will impact the influence operations of state governments and nonstate organizations alike. The findings reveal that while the ultimate intent of both state and non-state actors is to destabilize societies, their approaches …

Advancing Lymphoma Diagnosis In Histopathology Image Classification Using Multi Deep Learning Models, Ahmed Elaraby, Andrey Nechaevskiy, Aymen Saad

Iraqi Journal for Computer Science and Mathematics

Deep learning's rapid development is generating significant interest in its potential to improve medical imaging. It has shown promising results in detecting malignant lymphoma in histopathology medical images. Image classification methods are widely used to aid in making diagnoses from medical images. In recent years, deep learning methods have achieved high performance in detecting malignant lymphoma in histopathology images. This study proposes a novel approach to improving lymphoma diagnosis in histopathology images called the Lightweight Convolutional Neural Network (LWCNN). The proposed LWCNN model comprises multiple deep learning architectures, including a convolutional neural network (CNN) that has been trained to classify …

Benefits And Applications Of Learning With Virtual Reality, Michael W. Timm

Honors Program: Senior Projects (Public)

Education is a fundamental pillar of society. It equips students for employment and interpersonal relations. Virtual reality (VR) has emerged as a transformative technology in the field of education. The aim of this paper is to synthesize existing research in order to determine the benefits of utilizing virtual reality in a variety of education settings, such as K-12 classrooms, universities, and workplace training. This paper observes significant benefits of virtual reality in constructivist and experiential learning, gamified learning, and tailored practice. This analysis also finds that virtual reality is advantageous for educational accessibility, particularly for absentee students and impoverished students. …

Sensor Data Fusion For Air Quality Monitoring, Mirna Hesham

Theses and Dissertations

Since traditional air quality monitoring methods often rely on geographically sparse and costly air quality monitoring stations, image-based air quality method- ologies are recently offering a compelling alternative that utilizes images from sources like satellites, traffic cameras, and even smartphones to monitor pollution levels by using estimation models, image-processing techniques, and deep-learning models. In this thesis, we first conduct a systematic review, in which we categorize and discuss the existing literature work. Moreover, we introduce a novel, multi- modal dataset designed to address the limitations of existing datasets, which are restricted in size, geographical coverage, and fixed-scene imagery, impeding the …

The Hidden Cost Of Intelligence: Environmental Impacts And The Rise Of Green Ai Solutions, Hayin Tamut

ATU Scholars Symposium

Artificial Intelligence (AI) has rapidly transformed industries and daily life, but beneath its advancements lies a growing environmental concern. This paper explores the often-overlooked ecological footprint of AI technologies, focusing on real-world data and tangible impacts. Notably, training OpenAI’s GPT-3 model alone consumed approximately 1,287 megawatt-hours (MWh) of electricity—equivalent to the annual consumption of over 120 U.S. homes—and emitted 500 metric tons of CO₂, comparable to driving 112 gasoline-powered cars for a year.

Furthermore, the environmental cost extends beyond electricity consumption. Data centers powering AI require vast amounts of water for cooling, contributing to local water shortages—highlighted …

Comment La Digitalisation Affecte-T-Elle Les Interactions Sociales, Culturelles Et Économiques Au Maroc ?, Redouane El Majdouli

Dirassat

This study explores the multidimensional impact of digitalization on social, cultural, and economic interactions in Morocco. By leveraging modernization and social network theories, the analysis reveals how digital technologies transform social dynamics, facilitate innovation, and pose challenges in terms of access inequalities and privacy protection. Using a mixed- method approach combining quantitative surveys and qualitative analyses, the results show a growing adoption of digital technologies and their varied effects on Moroccan society. The proposed recommendations aim to maximize the benefits of digitalization while minimizing its risks, contributing to sustainable and inclusive development.

L'Utilisation Des Technologies Numériques Dans Les Musées Contemporains Au Maroc : Les Défis De La Préservation Culturelle, De L'Accès Et De La Justice Dans L'Utilisation De La Réalité Virtuelle Et Augmentée, Fadma Fangach

Dirassat

This study investigates the impact of immersive technologies, particularly Virtual Reality (VR) and Augmented Reality (AR), on visitor experiences at two distinct museums: the Museum of Modern and Contemporary Art Mohamed the Sixth and the Exhibition and Museum of the Prophet’s Biography and Islamic Civilization. Employing a qualitative approach and a comparative case study methodology, the research examines the employment of VR and AR within each museum, focusing on their respective modes of projection and exhibition. While the Museum of Modern and Contemporary Art Mohamed the Sixth uses VR to establish a virtual space online, the Exhibition and Museum of …

Sustainable Poultry Farming: A Concept Of Iot-Based Poultry Management System For Small-Scale Farmers, Ayodeji Akinsoji Okubanjo, Ignatius Kema Okakwu, Olufemi Peter Alao, Nurudeen Samuel. Lawal, Ayoola Abiola Babalola, Abisola Olayiwola

Al-Bahir

Conventional poultry management techniques are failing to meet increased demand for poultry products as the population continues to grow. As a result, this issue has become a major concern for small-scale farmers, particularly those in low-income areas, in terms of food security. One of the main reasons for this is that the farmers rely on intensive farming methods which are inefficient for automating daily poultry operations. However, intensive farming methods pose major environmental concerns to ecosystems and poultry health. Also, the environmental conditions, welfare, and productivity of poultry operations may be harmed by the global climate crisis and poultry waste …

Industry Applications Society And Conferences [President’S Message], Ayman El-Refaie

Electrical and Computer Engineering Faculty Research and Publications

No abstract provided.

Internet Of Things Devices Users’ Privacy Adherence: A Case Of Digital Ignorance, Akrasia Or Exhaustion?, Philip Bazanye, Walter F. Uys, Wallace Chigona

The African Journal of Information Systems

Internet of Things devices, such as those used in home automation, commercial and retail business, and smart cities, are vulnerable to attacks that affect all aspects of daily life. The upsurge in the use of IoT devices has increased the likelihood of cyber-attacks on end users. This research investigates the factors that influence IoT device users to adhere to privacy standards. This interpretivist exploratory research was guided by a three-phased approach. The interview questions were derived from the conceptual model and the constructs of Activity Theory, and themes were analyzed using deductive thematic analysis. The findings were elaborated with reference …

In Memoriam - Nora Sabelli: Master Orchestrator Of Grant Programs And Mentor For Advancing The Interdisciplinary Learning Sciences Field, Eric Hamilton, Jeremy Roschelle, Roy Pea, Barbara Means, Louis Gomez, Kim Gomez, Nancy Butler Songer

Education Division Scholarship

On Friday, September 6, 2024, the learning sciences field lost a giant in Dr. Nora Sabelli, 87 years old, a personal mentor to many researchers and an inspiration to so many learning scientists and STEM leaders. Nora’s first professional career was as a computational chemist, and later she became a passionate leader in research for improving STEM education. Nora’s time as a senior program officer at the National Science Foundation’s (NSF) Education and Human Resources (EHR) directorate was legendary; she was a force of nature who reshaped funding priorities for stronger science and a stronger connection of science to education …

Feasibility And Acceptability Of The Mazi Umntanakho Digital Tool In South African Settings: A Qualitative Evaluation, Catherine E. Draper, Caylee J. Cook, Elizabeth A. Ankrah, Jesus A. Beltran, Franceli L. Cibrian, Kimberley D. Lakes, Hanna Mofid, Lucretia Williams, Gillian R. Hayes

Engineering Faculty Articles and Research

To address the need for interventions targeting social emotional development and mental health of young children in South Africa, the Mazi Umntanakho (‘know your child’) digital tool was co-designed, and piloted with caregivers and 3–5-year-old children involved in home visiting programmes promoting early childhood development. The aim of this study was to qualitatively evaluate the feasibility and acceptability of this tool in four urban and four rural low-income communities, from the perspective of home visitors and caregivers. Focus groups were conducted with home visitors (n = 117) and caregivers (n = 72). Issues relating to the feasibility of …

Indoor Localization With Ensemble Machine Learning Via Visible Light Communication Channels, Alzahraa M. Ghonim, Wessam M. Salama

Journal of Engineering Research

An indoor localization system based on received signal strength, visible light communication (VLC) and several machine learning approaches is proposed in this paper. Our proposed framework is divided into two strategies. The first one is consisting of gathering our dataset based on MATLAB software to create indoor VLC channel model. While the second phase is depending on training the gained dataset using ensemble machine learning models. Specifically, random forest, decision tree and gradient boosting models. In order to evaluate the robustness of the proposed framework, several evaluation metrics are applied, specifically, training time, testing time, classification accuracy (CA), area under …

Understanding The Determinants Of Blockchain Adoption: An Empirical Study, Amarpreet Kohli, Nihar Kumthekar, Piyush Shah, Rebecca Jauch

Journal of International Technology and Information Management

Blockchain technology (BT) has the potential to enhance security and robustness of transactions through a distributed ledger bookkeeping process. This study employs technology-organization-environment (TOE) framework and threat-rigidity theory (TRT) to examine whether perceived disruption caused by COVID-19 pandemic significantly impacted the adoption of BT, and inclination to adopt BT in the US. The COVID-19 pandemic provided a unique backdrop, as it affected businesses across all industries, sizes, and geographies. Results show a non-significant effect of perceived pandemic disruption on the current stage of BT adoption and intention to adopt BT. However, disruption readiness positively influences the current stage of BT …

Comparing Vr And Tv In Nigeria And The U.S.: Impacts On Empathy, Engagement, And Enjoyment, Jinhee Yoo, Eugene A. Ohu, Radosław Mącik

Journal of International Technology and Information Management

This study (N = 170), involving participants from Nigeria and the U.S., investigated how different technologies (TV and VR) affect users' empathy (α = .93), engagement (α = .93), enjoyment (α = .93), preferences, and likelihood of technology use. Participants watched an animated documentary titled “Is Anna OK?” at two different time points, utilizing VR (Oculus Rift S) and TV, following which they completed measuring empathy, engagement, enjoyment, device preference, and usage likelihood. Analysis via one-way ANOVA and chi-square tests revealed that VR users reported significantly higher empathy and enjoyment compared to TV viewers, particularly on second viewing. Combining both …

An Educational Framework For The Disruptive Technologies And Their Integration In The Un Sdgs Curriculum, Tulsi Pawan Fowdur, Aishani Radhakeesoon

Journal of Educational Technology Development and Exchange (JETDE)

Disruptive technologies such as 5G, AI, IoT, cloud computing, and blockchain are revolutionizing life on our planet and at the same time contributing immensely towards sustainable development and the achievement of the UN SDGs. Nowadays, optimizing the management of resources in various fields such as manufacturing, education, health, transportation etc. in a much more efficient way is possible. This can be achieved by capturing a wealth of real-time data via IoT sensors with the ultra-reliable and low latency connections provided by 5G. Additionally, with cloud computing, blockchain, and AI, these real-time systems can securely transmit, store, and analyze a massive …

Predicting Crises On The African Frontier Stock Markets With Investor Sentiment Indicators: A Machine Learning Approach, David Korsah, Lord Mensah

Journal of International Technology and Information Management

This study examined the predictive ability of machine learning algorithms in identifying crises within African stock markets. The study employed seven distinct machine-learning models, analyzing historical stock prices from eight stock markets, three major sentiment indicators, and the exchange rates of local currencies against the US dollar, with each data spanning from May 1, 2007, to April 1, 2023. Extreme Gradient Boosting (XGBoost) emerged as the most effective algorithm for predicting crises. Historical stock prices and exchange rates were identified as the most critical features for prediction. On the sentiment side, investors’ perceptions of potential volatility on the S&P 500, …

Predicting Global Healthcare Supply Chain Delays: A Machine Learning Approach Leveraging Country-Level Logistics Metrics, Jeevan Sai Gali, Nima Molavi, Sepideh Alavi

Journal of International Technology and Information Management

In global healthcare logistics, ensuring the timely delivery of medical commodities is critical, particularly in low- and middle-income countries characterized by infrastructural limitations and operational uncertainties. This research introduces an advanced, data-driven predictive framework designed to forecast delivery delays by synthesizing granular, internal shipment-level data from the USAID Global Health Supply Chain Program (GHSC-PSM) with external country-level logistics capabilities indicators derived from the World Bank’s Logistics Performance Index (LPI). Rather than relying on retrospective trend analyses, this study employs machine learning algorithms such as Random Forest, XGBoost, Support Vector Machines (SVM), and Multi-Layer Perceptron (MLP) to detect …

Online Learning Transition: An Analysis Of Proactive Institutional Assistance In Reducing Difficulties During The Covid-19 Pandemic, Mohammed Yahya Alghamdi

BAU Journal - Science and Technology

The COVID-19 pandemic caused major changes in the education system, with a shift to online learning, and experience has shown that transitioning from face-to-face instruction is difficult. This study involved 80 academic staff members from Al-Baha University in Saudi Arabia to learn about the benefits, limitations, and institutional support of online education in the setting of an epidemic. The study answers two primary questions: The first study question was, What difficulties did instructors face when they switched to online instruction? While the second research question was, How did institutional support influence the transition to online instruction? The study’s research methodology …

Unveiling The Hidden Threat: How Wireless Networks Fuel Serious Cyber Attacks, Ibtesam Jomaa Hawi

Al-Esraa University College Journal for Engineering Sciences

The spread of wireless networks has led to an increase in serious cyber attacks due to their weak architecture. This article focuses on reevaluating cybersecurity in wireless network technology by integrating statistical information detection methods and artificial intelligence (AI) algorithms. To construct a wireless networking scenario that accurately reflects real-life conditions, we created a data fabrication that included four pre-existing anomalies as well as four newly introduced anomalies. The synthetic dataset created from these generation processes contains 20 thousand distinguishable values, which are later divided into training and validation sets. Using the strategy described before, we began to analyze the …