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Turkish Journal of Electrical Engineering and Computer Sciences

No abstract provided.

A Joint Optimization-Based Novel Attack For Genomic Beacon Reconstruction, Kousar Saleem, Si̇nem Sav

Turkish Journal of Electrical Engineering and Computer Sciences

Genomic data sharing has become an essential component of biomedical research, enabling large-scale collaborations and accelerating discoveries in human genetics. To balance the need for accessibility with privacy concerns, several controlled-access mechanisms have been proposed, including genomic beacons. Genomic beacons answer simple presence/absence queries about specific genetic variants. However, prior work has demonstrated that beacons remain vulnerable to genome reconstruction attacks, where an adversary can recover large portions of participants’ genomes using summary statistics. Building on insights from prior reconstruction attacks, we introduce an approach that unifies SNP correlation and allele frequency alignment objectives within a single-stage joint optimization framework. …

Automated Software Size Measurement Using Multilingual Domain-Adapted Language Models, Samet Tenekeci̇, Hüseyi̇n Ünlü, Burak Keçeci̇, Muhammed Efe İnci̇r, Onur Demi̇rörs

Turkish Journal of Electrical Engineering and Computer Sciences

Software Size Measurement (SSM) is crucial for estimating required project effort as well as budget and schedule. However, many small and medium-sized companies struggle to apply objective SSM due to limited resources and lack of expertise. This often leads to inaccurate estimates and project overruns. There is a need for practical, low-resource solutions that support these tasks without requiring expert involvement. Motivated by this challenge, this study proposes an automated software size measurement approach that formulates the measurement task as supervised regression over natural language requirements, using domain-adapted transformer models. We construct large-scale Turkish and English software engineering corpora to …

Agnostic Tomography Of Stabilizer Product States, Sabee Grewal, Vishnu Iyer, William Kretschmer, Daniel Liang

Computer Science Faculty Publications and Presentations

We define a quantum learning task called agnostic tomography, where given copies of an arbitrary state ρ and a class of quantum states C, the goal is to output a succinct description of a state that approximates ρ at least as well as any state in C (up to some small error ε). This task generalizes ordinary quantum tomography of states in C and is more challenging because the learning algorithm must be robust to perturbations of ρ. We give an efficient agnostic tomography algorithm for the class C of n-qubit stabilizer product states. Assuming ρ has fidelity at least …

Polyherbal Phytochemicals As Multi-Target Inhibitors Of Key Breast Cancer Proteins: A Computational Approach, Nadia Wahyuningsih, Nashi Widodo, Sri Rahayu, Muhaimin Rifa’I

Karbala International Journal of Modern Science

Breast cancer is a primary worldwide health concern, and conventional therapies often cause side effects. This study was performed to investigate the therapeutic potential of a polyherbal formulation containing Curcuma longa, Phyllanthus niruri, Ziziphus mauritiana, Nigella sativa, and Annona muricata as multi-target inhibitors against breast cancer protein targets using molecular docking and molecular dynamics in silico approach. Bioactive compounds were analyzed using Liquid Chromatography High-Resolution Mass Spectrometry (LC-HRMS) to identify the extract's phytochemicals. The compounds were examined for drug-likeness, membrane permeability, bioactivity, and toxicity. The inhibitory ability against the proto-oncogene pathway, which is commonly dysregulated and mutated in breast cancer, …

Synthesis And Characterization Of Ti-Enhanced F75/Ha Functionally Graded Materials Fabricated By Powder Metallurgy, Afrah M. Al Hussainey, Aseel Mustafa, Randa Kamel Hussain

Karbala International Journal of Modern Science

Functionally graded materials (FGMs) are a highly advanced class of biomaterials with graded structure and properties, enabling the fabrication of physiologically and mechanically compatible materials for use in various medical devices. This study aims to produce a functional-grade material based on a cobalt-chromium-molybdenum alloy (F75) reinforced with 4% titanium (Ti) and hydroxyapatite (HA). This will enhance the material's mechanical properties, corrosion resistance, and bioactivity, making it suitable for use as a bone substitute. The natural eggshells were washed, burnt, and chemically processed to form hydroxyapatite with a Ca/P proportion of 1.67. FTIR showed that phosphate and OH groups were separate, …

Data Centers In Mountain West Markets, 2026, Cason Noll, Krish Sharma, Maisoon Faris, Olivia K. Cheche, Caitlin J. Saladino, William E. Brown Jr.

Transportation & Infrastructure

This fact sheet reports on the distribution and geographic concentration of data centers across the Mountain West states of Arizona, Colorado, Nevada, New Mexico and Utah as of March 6th, 2026. Using data from DataCenterMap, this fact sheet examines the number of data centers in each Mountain West state and further analyzes market-level  distribution, defined as cities within each state where data centers are located. The data are used to compare state totals and to rank Mountain West markets from highest to lowest based on the number of data centers operating in that area.

Reasoning In Large Language Models Across Multilingual, Long-Context, And Symbolic Settings, Sina Bagheri Nezhad

Dissertations and Theses

Large Language Models (LLMs) are increasingly deployed as general-purpose reasoners, yet their reliability degrades in three settings that frequently arise in practice: multilingual inputs, long contexts, and symbolic or formally constrained domains. In multilingual settings, uneven training coverage produces substantial performance disparities and uncertain generalization to languages with little or negligible pretraining exposure. In long-context settings, relevant evidence may be sparsely distributed, and models exhibit the "lost-in-the-middle" phenomenon, undermining retrieval and multi-step synthesis. In symbolic settings such as mathematics, small arithmetic or logical slips invalidate solutions, and prose rationales are difficult to verify automatically.

This dissertation first characterizes these failure …

A Unified Non-Visual Explainable Ai Framework For Neonatal Pain Assessment, Md Imran Hossain

USF Tampa Graduate Theses and Dissertations

With the power of parallel processing, large datasets, and fast computational resources,deep neural networks (DNNs) have outperformed highly trained and experienced human experts in medical applications. However, the large global community of healthcare professionals, many of whom routinely face potentially life-or-death outcomes with complex medicolegal consequences, have yet to embrace this powerful technology. The major problem is that most current AI solutions function as a metaphorical black-box positioned between input data and output decisions without a rigorous explanation for their internal processes. With the goal of enhancing trust and improving acceptance of artificial intelligence– (AI) based technology in clinical medicine, …

Automating Cardiff Model Data Capture In Emergency Departments: Ambient Nlp Integration With Oracle-Cerner Fhir Systems, Simi Augustine, Marco A. Lopez, Jacquelyn Cheun, Chris Papesh

SMU Data Science Review

Violence and overdose events in Las Vegas occur at rates above the national average, with fewer than half of violent injuries reported to law enforcement [2,7]. The Cardiff Model offers a proven framework for standardized data collection and sharing between hospitals and public safety partners, yet many implementations still rely on manual entry. We propose an ambient triage pipeline integrated with Oracle-Cerner electronic health record systems to listen to nurse–patient dialogue, convert speech to text, extract Cardiff fields, and write standards-based FHIR Bundles for analytics. Using SMART on FHIR standards and Cerner Millennium APIs, the study evaluates whether ambient capture …

Anomaly Detection For Multi-System Bug Triage, Gibran Miguel Zavala Gamero, Hayoung Cheon, Mustafa Iqbal

SMU Data Science Review

Large-scale software systems produce vast volumes of logs and telemetry, making manual incident triage slow and error prone. This study presents an unsupervised anomaly detection pipeline that fuses logs, metrics, and traces through late fusion. Using Hybrid Ensemble modeling with Isolation Forest, and Long Short-Term Memory (LSTM) Deep Learning model, the system detects cross-service anomalies producing and assigning a composite triage score reflecting severity and impact. Ranked alerts are categorized into Critical, High, or Medium priorities for review. A retrieval-augmented generation (RAG) layer enriches results with contextual summaries for explainable triage. Evaluated on synthetic multi-service datasets, the pipeline …

Availability Model To Evaluate Ai Data Centers’ Role In Grid Stability, Troy Mcsimov, Trevor S. Kunz, Jeffrey Billo

SMU Data Science Review

The United States has made it clear; it is imperative that the US wins the global AI race. This paper focuses on one of the most challenging puzzle pieces surfaced at the POWER Data Center conference (San Antonio, Sept. 30.); for Electric Reliability Council of Texas (ERCOT) the limiting factor is not generation alone but the need to balance generation and load to preserve grid reliability.

The regulatory landscape fundamentally changed with the passage of Texas Senate Bill 6 in June 2025, which mandates new large loads must "contribute to the recovery of the interconnecting electric utility’s costs" (Texas Legislature, …

Why Text Prevails: Vision May Undermine Multimodal Medical Decision Making, Siyuan Dai, Lunxiao Li, Kun Zhao, Eardi Lila, Paul K. Crane, Heng Huang, Dongkuan Xu, Haoteng Tang

Computer Science Faculty Publications

With the rapid progress of large language models (LLMs), advanced multimodal large language models (MLLMs) have demonstrated impressive zero-shot capabilities on vision–language tasks. In the biomedical domain, however, even state-of-the-art MLLMs struggle with basic Medical Decision Making (MDM) tasks. We investigate this limitation using two challenging datasets: (1) three-stage Alzheimer’s disease (AD) classification (normal, mild cognitive impairment, dementia), where category differences are visually subtle, and (2) MIMICCXR chest radiograph classification with 14 non–mutually exclusive conditions. Our empirical study shows that text-only reasoning consistently outperforms vision-only or vision–text settings, with multimodal inputs often performing worse than text alone. To mitigate this, …

Human-Ai Collaboration In Marketing Brainstorming: A Meta-Analytic Investigation Of Implementation Approaches And Creative Outcomes, Michael Michaels

USF Tampa Graduate Theses and Dissertations

Marketing organizations face intensifying pressure to sustain creative output while managing constrained resources and accelerating digital content cycles. The integration of artificial intelligence into marketing brainstorming processes presents a promising response to this challenge; however, prior to this investigation, no comprehensive quantitative synthesis had established the overall effectiveness of human-AI collaboration in marketing creativity contexts or identified optimal implementation strategies.

This dissertation presents a systematic meta-analytic investigation of AI-enhanced brainstorming effectiveness in marketing contexts. Following PRISMA guidelines, 42 independent empirical studies (N = 2,166 participants) published between 2018 and 2025 were identified across four primary academic databases, coded for implementation …

Opportunities, Challenges, And Conspicuous Absences: An Integrative Review Of The Social Work Literature On Artificial Intelligence, Michael J. Massey, Ian G. Williams, Grace C. Polistina, Eathan A. Breaux

Publications and Research

INTRODUCTION: Social work discourse regarding artificial intelligence (AI) in practice, research, and education has proliferated over the last 5 years, reflecting both excitement over its potential and ambivalence about its ethical challenges. However, the extent to which social work is fully engaging with the structure of AI and its enormous impacts on the environment, labour, and distribution of power remains unclear.

METHODS: An integrative review of social work literature from 2020–2024 was conducted to address two research questions: 1) What is the nature of the social work discourse related to AI? 2) To what extent is the discourse …

Data-Driven Prioritization Of Cybersecurity Vulnerabilities Using Business Context, Pierce Read, George Antoniou

Faculty and Staff Publications & Presentations

No abstract provided.

Enhancing Low-Light And Noisy Images Using Gaussian Denoising And Clahe (Contrast-Limited Adaptive Histogram Equalization)., Daniel Adesoji

SACAD: Scholarly Activities

Abstract

In digital imaging Low light image improvement is a crucial issue, with applications in medical imaging, surveillance and digital imaging. Images captured under substandard illumination usually appear dark and noisy: contrast is lower, hiding crucial details, while ISO (international Organization for Standardization) settings introduce grainy noise that devalue quality. These issues make images a problem for both human interpretation and automated vision system.

Traditional improvement methods such as histogram equalization and Retinex -based techniques enhance brightness but usually cause artifacts to boost noise. Deep learning approaches achieve strong results but require large datasets, heavy computation, and may fail to …

Factors For Patient Trust And Acceptance Of Medical Artificial Intelligence, Ana Bracic, Kayte Spector-Bagdady, Sophie Towle, Rina Zhang, Cornelius A. James, Nicholson W. Price Ii

Articles

Artificial intelligence (AI) is increasingly used in clinical care, but widespread adoption requires patient trust. Trust may be enhanced through systemic governance mechanisms or frontline clinicians providing a human in the loop for AI oversight. However, it is unclear how different approaches specifically influence patient trust in the use of medical AI. The objective is to determine the extent to which patient trust in and choice of medical scenarios involving AI are associated with governance mechanisms, clinician presence, performance, and data quality.

Privacy Preserving Anomaly Detection System For Der Solar Inverters, Tanzim Jim Hassan, Prakash Ranganathan

Graduate Research Achievement Day Posters

Reliable operation of solar inverters depends on maintaining a stable frequency. Recent cyber attacks on solar DERs are concerning and increase the likelihood of such stealthy attacks leading to anomalies in DER. Therefore, a robust anomaly detection system (ADS) is crucial for solar inverters used in distributed energy resources (DERs), enabling timely detection and correction of frequency anomalies. Additionally, preserving data privacy is essential for the security and reliability of the power grid. This paper proposes a privacy-preserving anomaly detection system (PP-ADS) based on a multi-stage hybrid machine learning (MSHML) model specifically designed for solar inverter data in DER environments. …

Do Emotions Matter In Ai? The Mediating Role Of Emotional Response Between Perceived Risk And Trust, Areej Babiker, Mohamed Basel Almourad, Sameha Alshakhsi, Magnus Liebherr, Raian Ali

All Works

Research shows that trust in AI is influenced by socio-ethical considerations, technical features of AI systems, and user characteristics. Yet, the mediating role of emotional response between perceived risk and trust remains underexplored, particularly across different AI contexts. This cross-sectional vignette experiment design aims to explore the relationship between users' perceived potential risk, emotional response, and trust in AI, and examine how these relationships vary across different levels of automation and criticality. An online survey included a total of 639 participants including 316 from the UK and 323 from Arab Gulf Cooperation Council (GCC) countries. Participants rated their perceived risk, …

Multiple Q-Dqn Algorithm Based Rumor Control In Social Networks, Zainab Hasan, Huda Naji Nawaf

Journal of Intelligent Informatics, Networking, and Cybersecurity

Malicious rumours on social media platforms like Facebook, Twitter, and others can be widely disseminated because of social problems. It is challenging to manage a rumour once it gains growth and quickly moves throughout a network. One of the main issues with information dissemination is figuring out how to reduce the propagation of rumours within a social network. A deep reinforcement learning technique might be an effective strategy to manage the rumour issue. Deep Q learning network (DQN) has been used in the literature to mitigate rumors by selecting a blocker at each time step. In this work, the proposed …

Markets, Agency, And Trust: Ai Agents And The Knowledge Problem, Brennan Mcdavid, Lynne Kiesling, David Chassin

Philosophy Faculty Articles and Research

Artificial intelligence (AI) is transforming market participation, raising key epistemological questions: Do AI agents enhance or diminish the aggregation of local, private, and tacit knowledge Hayek saw as essential to market processes? How does trust in both markets and AI shape willingness to engage in AI-mediated exchange? This paper examines these issues through market epistemology, agency relationships, and trust epistemology, analyzing how agentic AI reshapes the knowledge problem and principal-agent dynamics. Applying this framework to transactive energy markets, we show that AI shifts decision-making from human cognition to algorithmic processes that require user trust despite epistemic opacity, although it is …

Impediments To Transforming The Healthcare Delivery System: Shifting The Paradigm From Provider Centric To Patient Centric, Elizabeth A. Regan, Manasa Devi Chinta

Faculty Publications

Introduction: 

Stated aims for digital healthcare transformation frequently cite goals for better coordinated patient-centric systems. However, despite advances in medical science, digital technologies, health policies, and billions of dollars invested over the past 25 years, most healthcare providers are far from fully realizing the demonstrated benefits of today's digital technologies for improving patient care. Sharing information across healthcare systems remains challenging. Problems with fragmentation, quality, inequities, and rising costs of care delivery persist. A recent study of 1,026 U.S. hospital systems found that only 15.8 percent achieved a digital maturity level needed to provide digitally enabled healthcare services to better …

Review On Data Privacy And Security For Iot-Based Multifunctional Layers Of Cyber-Physical Systems In Smart Grids, Mohammad Kamrul Hasan, Md Mehedi Hasan, Nabeel Al-Qirim, Siti Norul Huda Sheikh Abdullah, Shayla Islam, Md Abdur Razzaque

All Works

Smart grid cyber-physical systems (SG-CPS) are intelligent platforms that incorporate IoT-enabled multifunctional layers including the physical, perception, communication, cyber, and application layers. It includes supervisory control and data acquisition, wide-area measurement systems, and advanced metering infrastructure for remote data aggregation, monitoring, and control operations. From an environmental perspective, these green technologies support two-way operations, which generate and transmit data over wired and wireless communication systems. However, this critical infrastructure faces data privacy and cybersecurity challenges. Hence, extensive research is required to address data privacy and security gaps to strengthen national grid cybersecurity and reduce economic losses. Therefore, this review highlights …

An Ai Approach To Differentiating Lung Squamous Cell Carcinoma From Metastases Of Other Origins, Mark G Evans, Jennifer Ribeiro, Todd Maney, Anthony Helmstetter, Jennifer Johnson, Anthony Karnezis, Casey Bales, George Sledge, David Spetzler, Ari Vanderwalde, Matthew Oberley, Balazs Halmos, Hossein Borghaei, Farah Abdulla, David Bryant, Fred Hirsch, Hassan Ghani

Department of Medical Oncology Faculty Papers

IMPORTANCE: Distinguishing primary lung squamous cell carcinoma (SCC) from squamous metastases to the lung is a clinical challenge due to histopathologic similarities. Accurate diagnosis is essential to guide treatment decisions.

OBJECTIVE: To assess the utility of an artificial intelligence (AI) approach that includes evaluation of key orthogonal evidence in distinguishing primary lung SCCs from metastatic tumors of other tissue origins.

DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study used GPSai, a tissue-of-origin AI model run automatically on each sample submitted for molecular profiling, to flag potential misdiagnoses among research-eligible cases submitted as lung SCC. Molecularly profiled cases within the Caris Life …

Efficient Active Training For Deep Lidar Odometry, Beibei Zhou, Zhiyuan Zhang, Zhenbo Song, Jianhui Guo, Hui Kong

Research Collection School Of Computing and Information Systems

Robust and efficient deep LiDAR odometry models are crucial for accurate localization and 3D reconstruction, but typically require extensive and diverse training data to adapt to diverse environments, leading to inefficiencies. To tackle this, we introduce an active training framework designed to selectively extract training data from diverse environments, thereby reducing the training load and enhancing model generalization. Our framework is based on two key strategies: Initial Training Set Selection (ITSS) and Active Incremental Selection (AIS). ITSS begins by breaking down motion sequences from general weather into nodes and edges for detailed trajectory analysis, prioritizing diverse sequences to form a …

Identifying And Mitigating Api Misuse In Large Language Models, Terry Yue Zhuo, Junda He, Jiamou Sun, Zhenchang Xing, David Lo, John Grundy, Xiaoning Du

Research Collection School Of Computing and Information Systems

API misuse in code generated by large language models (LLMs) presents a serious and growing challenge in software development. While LLMs demonstrate impressive code generation capabilities, their interactions with complex library APIs are often error-prone, potentially leading to software failures and vulnerabilities. In this paper, we conduct a large-scale study of API misuse patterns in LLM-generated code, analyzing both method selection and parameter usage across Python and Java, using three representative LLMs (StarCoder-7B, Qwen2.5-Coder-7B, and GitHub Copilot). Based on extensive manual annotation of 3,209 method-level and 3,492 parameter-level misuses, we identify and categorize four recurring misuse types by building on …

(Si16-11) Seasonal And Temporal Optimization Of Solar Energy Harvesting In Smart Iot Lighting Infrastructure, Abhijit Paul, Rishabh Pipalwa, Sabyasachi Mondal North - Eastern Hill University, Shillong, India

Applications and Applied Mathematics: An International Journal (AAM)

This study investigates the seasonal and temporal optimization of solar energy harvesting in a smart IoT-enabled streetlighting infrastructure by focusing on the theoretical determination of optimal solar panel tilt angles. The proposed system incorporates auto-adjusted solar panels integrated with an IoT network comprising sensors, microcontrollers, and streetlights. A key innovation lies in the implementation of a modified MQTT communication protocol, which enables efficient, localized decision-making and data exchange among components. Simulation results indicate that the modified MQTT protocol significantly reduces communication delay and power consumption compared to the conventional MQTT approach, thereby enhancing the overall system performance. Detailed analysis of …

An Odd Protocol For An Agent-Based Model Of Hepatitis C Virus Transmission With Inter- And Intra-Group Structural And Behavioural Heterogeneous Syringe-Sharing Networks (5), Seun Ale, Que Nguyen Dr., John D. Kelleher Prof., Elizabeth Hunter Dr.

Reports

The model described in this ODD is an agent-based model of hepatitis C virus (HCV) transmission among people who inject drugs (PWID). The model incorporates structural heterogeneity through a three-group interaction framework and behavioural heterogeneity through group-specific syringe-sharing rates with additional intra-group variability. The syringe-sharing population in the model is divided into core, inner, and outer circle groups representing individuals with high, moderate, and low levels of syringe-sharing interaction intensity, respectively. In addition to differences in the number of daily interaction opportunities across groups, agents in each group are assigned syringe-sharing probabilities that vary at the individual level around their …

The Application, Construction, And Validation Of Hidden Markov Model Profiles For Carbonic Anhydrase Enzymes, Samuel F. Kaplan

Master's Theses

Carbonic anhydrases (CAs) catalyze the reversible hydration of CO2 and have evolved independently at least eight times, resulting in structurally distinct enzyme families (α, β, γ, δ, ζ, η, θ, ι). Traditional sequence alignment methods struggle to classify these convergently evolved proteins because their sequential similarity does not reliably indicate functional or evolutionary relationships. Many CA sequences in public databases are annotated generically without family assignments, and prior computational approaches have focused predominantly on the three well characterized families (α, β, γ), leaving the five recently discovered classes without robust classification tools. Family level assignment is often a prerequisite for …