Digital Commons Network^™

A Novel Hybrid Intrusion Detection Model: A New Metaheuristic Approach For Feature Selection Based On Ai Techniques For Cyber Threat Detection, Maryam Mahdi Alhusseini, Alireza Rouhi

Iraqi Journal for Computer Science and Mathematics

The rapid increase in internet usage, digital transformation, and the rise of interconnected devices have greatly expanded the attack surface, introducing new and evolving cybersecurity challenges. Conventional security solutions frequently have difficulty adjusting to complex threats and the vast dimensionality of network traffic data, particularly in the case of imbalanced datasets. To tackle these challenges, this research introduces a Hybrid Intrusion Detection System (HyIDS-EVO) that combines the Energy Valley Optimizer (EVO) for feature selection and dimensionality reduction with machine learning classifiers, which include Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and K-Nearest Neighbors (KNN). The system’s effectiveness …

Developing A High-Throughput Machine Learning Screening Platform For Genetic And Pharmacological Modulators On Caenorhabditis Elegans Seizure Behavior, Paul A. Scarpinato

Electronic Theses and Dissertations 2020 - Present

Epilepsy is a chronic neurological disorder characterized by recurrent seizures that affect over 50 million people worldwide, including more than 3.5 million in the United States. Seizures arise from a disrupted balance between excitation and inhibition. Although antiepileptic drugs (AEDs) remain the primary treatment to restore the excitation and inhibition balance, approximately 30% of patients experience drug-resistant epilepsy, highlighting the need for additional antiseizure medications. The Caenorhabditis elegans electroshock assay was developed in relative alignment with the rodent maximal electroshock seizure (MES) test, the gold standard for preclinical AED screening, provides a cost-effective platform for prescreening candidate AEDs.

This work …

Multiplexed Integrin Detection And Cancer Cell Classification Using Multicolor Gap-Enhanced Gold Nanorods And Machine Learning Algorithm, Suprava Shah, Reed Youngerman, Alberto Luis Rodriguez-Nieves, Mitchell Lee Taylor, William Rodney Bantom, David Thompson, Jingyi Chen, Yongmei Wang, Xiaohua Huang

Chemistry & Biochemistry Faculty Publications and Presentations

Integrins, cell-surface adhesion receptors involved in tumor progression, invasion, and metastasis, serve as crucial biomarkers for cancer diagnosis and therapeutic targeting. Multiplexed detection of integrins and cancer cell classification at the single-cell level allows for comprehensive profiling, facilitating precise identification and categorization of tumor cells that are heterogeneous in integrin expression and cell subtype. In this study, we developed a five-plex detection platform and demonstrated integrin profile for cancer cell classification leveraging surface-enhanced Raman scattering (SERS) with gap-enhanced gold nanorods (GENRs) in conjunction with advanced computational analysis. Specifically, we synthesized GENRs bearing five distinct Raman nanotags, each producing a unique …

Interpretable Machine Learning For Cardiovascular Risk Prediction: Insights From Nhanes Dietary And Health Data, Md Ahiduzzaman, Md Nahid Hasan

Faculty Publications

Background: Cardiovascular diseases (CVD) are one of the leading global causes of death, which requires an accurate early prediction. This study aimed to develop transparent machine learning (ML) models using National Health and Nutrition Examination Survey (NHANES) data from 2017–2023 to predict CVD risk based on dietary and health factors.

Methods: We analyzed data from 12,382 adults (aged 18 and older) from NHANES 2017–2023, including 41 dietary, anthropometric, clinical, and demographic variables. Recursive Feature Elimination (RFE) was used to select an optimal subset of 30 predictors. To address substantial class imbalance in the outcome, we applied the Random Over-Sampling Examples …

Advancing Road Safety Through Data Integration, Location Accuracy, And Risk-Based Route Optimization, Lars Skaug

Electronic Theses and Dissertations 2020 - Present

This dissertation advances transportation safety through three interconnected contributions that address fundamental challenges in crash data analysis and application. First, we present a comprehensive survey of road crash analysis methodologies, tracing the evolution from traditional statistical approaches to modern machine learning techniques while identifying critical gaps between research sophistication and practical implementation. This survey reveals that while methodological advances have been substantial, ongoing data quality issues, particularly in location accuracy and completeness, limit the effectiveness of even the most sophisticated analytical approaches.

Building on these findings, we develop a novel system to validate and correct crash location data using multi-modal …

Possibilities Of Digitizing And Applying Artificial Intelligence To National Occupational Classification (Noc-2025) In Uzbekistan, Shohrux Nurali O‘G‘Li Narzullayev

Chemical Technology, Control and Management

This article examines the process of digitizing National Occupational Classification (NOC-2025) in Uzbekistan, developed on the basis of the International Standard Classification of Occupations (ISCO-08), and the possibilities of applying artificial intelligence technologies to it. Although this classification exists today in a national form, and its digitization and the introduction of artificial intelligence elements to it based on modern technologies remain a pressing issue. In order to digitize the classification, international systems such as the International Standard Classification of Occupations (ISCO-08, ILO), European Skills, Competences, Qualifications and Occupations (ESCO), Occupational Information Network (O*NET, USA) and National Occupational Classification (NOC, Canada) …

Image Captioning Through The Lens Of The Gricean Maxims: Generating Meaningful And Relevant Image Descriptions, Annika Lindh

Doctoral

Image captioning models enable us to automatically generate natural language image descriptions for previously unseen images. It combines the two fields of computer vision and natural language generation, allowing models to interpret the con tent of an image and communicate that knowledge through natural language text.

Research into image captioning has the potential benefit of reducing the gap in digital information availability between fully sighted individuals and those who are visually impaired. However, automatically generated captions often fail to provide the required level of detail and specificity to achieve this goal. Furthermore, current standard evaluation methods are insufficient at measuring …

Investigating Characteristic Droplet Size Distributions In Large Eddy Simulations Of Stratocumulus Clouds, Nithin Allwayin, Daniel J. Miller, Kamal Kant Chandrakar, Michael L. Larsen, Raymond A. Shaw

Michigan Tech Publications

Cloud processes relevant to radiative and precipitation properties depend on the shape of the cloud droplet size distribution. Recent holographic observations revealed that cloud droplet populations do not have the same size distribution shapes throughout but form regions of characteristic distributions with similar microphysical properties. We investigate the existence and properties of these characteristic distributions within Large-Eddy Simulations of stratocumulus clouds using Lagrangian and bin microphysics schemes. Distribution types are identified, revealing localized characteristic distributions that vary on the scale of the largest convective cell for simulations with bin microphysics. The results from the Lagrangian microphysics scheme hint at similar …

Face Recognition Using Optimized Multi-Task Cascaded Cnn In Artificial Intelligence Systems, Sami Abduljabbar Rashid, Mustafa Maad Hamdi, Salah Ayad Jassim, Lukman Audah, Baraa Saad Abdulhakeem, Mohammed Salah Abood, Ahmed Adil Nafea

Baghdad Science Journal

Improving the accuracy level of face recognition system is still an open research area where it consists of certain limitations to attain maximum accuracy. In earlier days, several algorithms are developed using machine learning and deep learning models, but still, they need improvement in terms of accuracy. For that purpose, in this article a novel CNN model called the optimized multi cascaded CNN (OPT-MTC-CNN) in the artificial intelligence system is developed, which helps to overcome the accuracy issues in face recognition system. The algorithms that are incorporated in this model are Fully Convolutional Network (FCN), Convolutional Neural Network (CNN), and …

Dramatic Biases In Terrestrial Nitrogen Fixation In Earth System Models Revealed By Natural Isotope Signatures, Maoyuan Fang, Shushi Peng, Philippe Ciais, Daniel S. Goll, Benjamin Z. Houlton, Ying-Ping Wang, Yilong Wang, Pan Liu, Joshua B. Fisher, Pierre Regnier

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Biological nitrogen fixation (BNF) is the primary input of new reactive nitrogen to natural terrestrial ecosystems. However, this flux is poorly constrained due to its unclear drivers and associated control mechanisms. Here, we extend the existing theory of nitrogen (N) isotope mass balance to estimate BNF rates and then use a Bayesian approach to constrain the BNF rates in natural terrestrial ecosystems by using measurements of natural N-isotope ratios (δ¹⁵N) in plants (δ_P) and soil (δ_S). Together with pairwise δ_P and δ_S measurements from 18 forest sites covering diverse climates and thousands …

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

School of Engineering, Computing and Mathematics

From a psychological perspective, human behaviourreflectsunderlyingthoughtsand decision-making patterns, for example, consumer behaviour may correlate with the purchase decisions. In the fast-evolving e-commerce industry, predicting user behaviouris essential for enhancing marketing strategies, improving customer experiences, and increasing sales. However, traditional heuristic (e.g.market basket analysis) approaches to analysebuyer behaviour are often rigid and fail to adapt to complex consumer interactions. This research work develops a predictive model that analysesuser 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 theprimarymachine learning algorithm to estimate the likelihood of …

Improved Streamflow Forecasting Through Swe-Augmented Spatio-Temporal Graph Neural Networks, Akhila Akkala, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi, Pouya Hosseinzadeh, Ayman Nassar

Computer Science Student Research

Streamflow forecasting in snowmelt-dominated basins is essential for water resource planning, flood mitigation, and ecological sustainability. This study presents a comparative evaluation of statistical, machine learning (Random Forest), and deep learning models (Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Spatio-Temporal Graph Neural Network (STGNN)) using 30 years of data from 20 monitoring stations across the Upper Colorado River Basin (UCRB). We assess the impact of integrating meteorological variables—particularly, the Snow Water Equivalent (SWE)—and spatial dependencies on predictive performance. Among all models, the Spatio-Temporal Graph Neural Network (STGNN) achieved the highest accuracy, with a Nash–Sutcliffe Efficiency (NSE) of 0.84 …

Foundations Of Artificial Intelligence In Healthcare Diagnostics: A Systematic Survey, Raghad Tariq Al-Hassani

Al-Esraa University College Journal for Engineering Sciences

Artificial Intelligence (AI) is becoming the cornerstone of the future of healthcare diagnostics, that has to ability to change the healthcare diagnostic landscape in terms of diagnostic accuracy, speed, and availability. This systematic review investigates the basic methods, tools, applications, and challenges involved in the integration of AI in diagnostic medicine. It emphasizes the using of machine learning models, deep learning networks (e.g., CNNs), NLP for clinical documentation, and smart computing infrastructures, such as edge device and IoMT. They are making possible real-time, data-driven decision making that is already at human-expert-level performance or, in some cases, even better (in the …

Towards Autonomous Energy Management: Machine Learning For Effective Auditing And Optimization, Sameh O. Abdellatif, Sherif Ashraf, Mira Mohsen

Electrical Engineering

This study presents a fully automated procedure for energy management and auditing, applicable to a diverse range of residential and commercial loads, leveraging machine learning techniques across three key phases: load classification, benchmarking, and smart monitoring. The model effectively categorizes energy loads based on consumption patterns, establishes performance benchmarks through historical data analysis, and employs real-time monitoring to identify inefficiencies and predict future energy usage. Evaluating the model through four distinct case studies demonstrates its capability to optimize energy consumption in a techno-economic manner, achieving significant energy savings of 34.73 MWh/year for essential loads in Egypt, 215.67 MWh/year for HVAC …

Theoretical Investigation And Practical Validation Of Ai Based Fault Detection In Bjt Amplifier Circuits, Sameh O. Abdellatif, Kyrillos Selim, Ahmad Alaa, Ola Hassan

Electrical Engineering

Analog circuits are essential to electronic systems due to their variety of critical functions. Catastrophic faults, such as short- and open-circuit faults, can stop the circuit from functioning. Meanwhile, parametric faults such as drift due to temperature fluctuations, noise in components, and power supply issues cause the circuit to operate beyond its range. Such faults can harm the circuit and its constituent parts. Therefore, it is crucial to identify and address the issues brought on by these faults. Common traditional fault diagnosis methods that rely on manual testing and visual assessment are lacking in complex electronic systems and faults. Such …

Design Optimization And Scaling Of Coreless Afpm Machines Using Hybrid Fea-Based Differential Evolution And Machine Learning, Matin Vatani, David R. Stewart, Donovin D. Lewis, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

This paper presents a machine learning (ML) based design framework for the fast and accurate optimization of coreless axial flux permanent magnet (AFPM) machines. Although the absence of magnetic cores eliminates material nonlinearity, the design process remains highly nonlinear due to the complex influence of geometric parameters. To overcome the computational challenges of finite element analysis (FEA)-based optimization, a series of multi-objective differential evolution (MODE) optimizations were conducted across various machine sizes at constant power output. The resulting design data was used to train an artificial neural network (ANN), enabling rapid prediction of machine performance without the need for repeated …

Leveraging Machine Learning And Causal Inference For Loan Default Prediction, Luca Guida

Doctoral Dissertations and Master's Theses

This research explores a systematic application of machine learning techniques combined with causal inference to predict loan defaults in peer-to-peer lending. Accurately forecasting loan defaults is crucial for mitigating financial risk and optimizing lending strategies. This analysis is based on multiple datasets of loan applications spanning over a decade, containing detailed financial and credit information about borrowers. Beginning with extensive Exploratory Data Analysis (EDA) coupled with scaling strategies, the research identifies key trends in loan performance across a large number of factors, such as interest rates or borrower creditworthiness, and one objective is to determine from the many available predictors …

Optimization Of A Mobile Imaging System To Aid In Evaluating Patients With Oral Lesions In A Dental Care Setting, Ruchika Mitbander, Jennifer Carns, Richard A Schwarz, Sean Anderson, Geethanjali Vipulanandan, Miriam Hernandez, Amelia E Hartzell, Jun Jiao, Ilona Rutkowska, Safia Durab, Sahar Sadeghi, Ashlee D Clayton, Nancy Bass, Loganayaki Anandasivam, Rachel A Giese, Ann M Gillenwater, Nadarajah Vigneswaran, Rebecca Richards-Kortum

Faculty, Staff and Student Publications

Significance: Oral cancer is often diagnosed at an advanced stage even though the oral cavity is easily accessible for examination. There is a need for tools to support early detection and help non-expert clinicians make appropriate referral recommendations.

Aim: We aim to evaluate a mobile detection of oral cancer (mDOC) smartphone-based autofluorescence imaging system by collecting data in a representative, low-prevalence population and optimizing a referral algorithm suitable for deployment in community dental settings.

Approach: Data were collected from patients at a community dental clinic using the mDOC system, which captures white light and autofluorescence images. A multi-input deep learning …

Explainabledetector: Exploring Transformer-Based Language Modeling Approach For Sms Spam Detection With Explainability Analysis, Mohammad Amaz Uddin, Muhammad Nazrul Islam, Leandros Maglaras, Helge Janicke, Iqbal H. Sarker

Research outputs 2022 to 2026

Short Message Service (SMS) is a widely used and cost-effective communication medium that has unfortunately become a frequent target for unsolicited messages - commonly known as SMS spam. With the rapid adoption of smartphones and increased Internet connectivity, SMS spam has emerged as a prevalent threat. Spammers have recognized the critical role SMS plays in today's modern communication, making it a prime target for abuse. As cybersecurity threats continue to evolve, the volume of SMS spam has increased substantially in recent years. Moreover, the unstructured format of SMS data creates significant challenges for SMS spam detection, making it more difficult …

Machine Learning For Dissolved Oxygen Prediction In The Manawatu River Catchment, New Zealand, Imokhai T. Tenebe, Eunice O. Babatunde, Nkpa M. Ogarekpe, Egbe Etu Etu, Joshua Emakhu, Kindness E. Etu, Obiora B. Ezeudu, Oluwarotimi S. Oladele

Faculty Research, Scholarly, and Creative Activity

Uncontrolled anthropogenic activities have increased river pollution, resulting in poor water quality and a growing need for continuous monitoring, which is time-consuming and costly. This study evaluated the accuracy of machine learning (ML) models in predicting dissolved oxygen (DO) levels, aiming to reduce field monitoring efforts and minimize the number of measured parameters. Regression models (Huber, Linear, Ridge), Bagging methods (Extra Trees, Random Forest, Decision Trees), and Boosting techniques (Gradient Boosting, Light Gradient Boosting, AdaBoost) were tested using data from three sites in the Manawatu Catchment, New Zealand. Data spanning 1989–2014 included 12 water quality variables and was split into …

Deep Learning For Hate Speech Detection: A Comparative Study, Jitendra Singh Malik, Hezhe Qiao, Guansong Pang, Anton Van Den Hengel

Research Collection School Of Computing and Information Systems

Automated hate speech detection is an important tool in combating the spread of hate speech, particularly in social media. Numerous methods have been developed for the task, including a recent proliferation of deep-learning based approaches. A variety of datasets have also been developed, exemplifying various manifestations of the hate-speech detection problem. We present here a largescale empirical comparison of deep and shallow hate-speech detection methods, mediated through the three most commonly used datasets. Our goal is to illuminate progress in the area, and identify strengths and weaknesses in the current state-of-the-art. We particularly focus our analysis on measures of practical …

Iot-Enabled Machine Learning Framework For Prediction Of Eutrophication, Hocine Dai, Akli Abbas, Houssam Eddine-Othman Lachemat, Aicha Aid

Iraqi Journal for Computer Science and Mathematics

This study presents an innovative predictive monitoring framework that integrates the Internet of Things (IoT) with advanced machine learning (ML) techniques to model the relationship between oxidized nitrate (NOX)—employed as the sole predictor—and chlorophyll a (CHLA), a key proxy for algal biomass. By utilising a single optimally selected parameter, the approach significantly reduces sensor deployment complexity and instrumentation costs, while minimising data acquisition and computational requirements. Logarithmic and Yeo-Johnson transformations were applied to the predictor and target variables, respectively, to address distributional skewness and enhance variance homogeneity. An optimised Random Forest model demonstrated strong predictive performance, achieving a coefficient of …

Ensemble Learning-Based Vulnerability Detection And Forecasting, Hamdan Alshehri

University of Bisha Journal for Basic and Applied Sciences

The problem of identifying threats in cyberspace is basically a classification problem for machine learning, which is also a need of the hour. While a large number of works related to vulnerability detection have been reported recently, the majority of the works have been focused on IT systems. Various factors related to these systems are considered for the classification of risk levels in these works, and appropriate recommendations about expected risks and vulnerabilities in the future will be given to their users. This work aims to detect possible risks and vulnerabilities and notify the user in a timely manner using …

Enhanced Pmu Data Imputation Using Variational Autoencoder: A Novel Approach For Improved Accuracy, Subin Koshy, Sunitha Rajan, Elizabeth P. Cheriyan

Turkish Journal of Electrical Engineering and Computer Sciences

Data collected from various measurement devices, such as phasor measurement units (PMUs), form the backbone of power system monitoring algorithms such as state estimation algorithms. Even though modern PMUs are efficient, data inaccuracies and loss can still occur due to problems like equipment malfunctions, communication issues, and hardware or software glitches. Missing values in the datasets can also affect the overall quality, impacting the algorithms dependent on them. This article introduces a novel technique for real-time imputation of missing data using variational autoencoder (VAE) and utilizing the latent space representation acquired from VAE. The method has demonstrated enhanced accuracy in …

Non-Invasive Detection Of Choroidal Melanoma Via Tear-Derived Protein Corona On Gold Nanoparticles: A Machine Learning Approach, Hakimeh Rakhshandeh, Ahmad Nasiraei, Hamid Riazi-Esfahani, Babak Masoomian, Fariba Ghassemi, Mojtaba Arjmand, Saeed Heidari Keshel, Fatemeh Atyabi, Rassoul Dinarvand

Wills Eye Hospital Papers

This study investigates the feasibility of using tear sample analysis, based on protein corona formation on gold nanoparticles combined with electrospray ionization mass spectrometry (ESI-MS) and machine learning techniques, as a non-invasive approach for the detection of choroidal melanoma. The aim is to assess whether protein-nanoparticle interactions can support early and reliable identification of this ocular condition. Tear samples were collected using Schirmer strips from six healthy individuals and six patients diagnosed with choroidal melanoma, with subsequent augmentation to 18 samples per group. Gold nanoparticles (AuNPs, ~ 20 nm) were synthesized via citrate reduction and incubated with tear samples to …

Electromagnetic Simulations Of Antennas On Gpus For Machine Learning Applications, Murat Temi̇z, Vemund Bakken

Turkish Journal of Electrical Engineering and Computer Sciences

This study proposes an antenna simulation framework powered by graphics processing units (GPUs) based on an open-source electromagnetic (EM) simulation software (gprMax) for machine learning applications of antenna design and optimization. Furthermore, it compares the simulation results with those obtained through commercial EM software. The proposed software framework for machine learning and surrogate model applications will produce antenna data sets consisting of a large number of antenna simulation results using GPUs. Although machine learning methods can attain the optimum solutions for many problems, they are known to be data-hungry and require a great deal of samples for the training stage …

Novel Biological Prompting Technology: Automatic Directional Control By Eye-Tracking Sensor, Guannan Liu, Justin Wang, Hsiao Chun Wu, Gaojian Huang, Anil Kumar, Yiyan Wu, Shih Yu Chang, Yue Luo

Faculty Research, Scholarly, and Creative Activity

Biological prompting or human teleoperation have been appealing to many researchers lately. Hands-free and voice-free command or control of vehicles, aircraft, machines, robots, etc. would provide huge convenience to users and allow them for multi-tasking. In this work, we would like to dedicate to a new biological prompting function, namely automatic directional control using instantaneous eye gaze data. In this work, we would like to investigate how to apply advanced machine-learning models to design novel automatic gaze-direction identification techniques. We explore the difficulty, namely infliction with outliers and variations, in dealing with eye-tracking data and propose to transform the original …

Machine Learning Approach For Fake Image Forensics, Abdullah Mujawib Alashjaee

University of Bisha Journal for Basic and Applied Sciences

Digital forensics is crucial in investigating and analyzing digital evidence, including images, to uncover potential crimes and identify manipulated or fraudulent content. A significant challenge in digital forensics is distinguishing between authentic and manipulated images. This paper applies a popular pre-trained deep learning model, VGG16, for feature extraction in real vs. fake image detection and various machine learning (ML) models as classifiers. The feature extraction part until the flatten layer of the VGG16 model is utilized to extract discriminative features that capture high-level image representations comprising local and global patterns. ML models, including Random Forest, Logistic Regression, Decision Tree, and …

Novel Convolution Neural Network For Enhanced Network Attack Pattern Recognition, Salahaldeen Duraibi

University of Bisha Journal for Basic and Applied Sciences

Intrusion detection plays a critical role in safeguarding computer networks from malicious activities and unauthorized access. Traditional machine learning (ML) models have been widely employed for intrusion detection, but their effectiveness can be limited due to the complex and evolving nature of network attacks. An efficient Convolutional Neural Network (CNN) that utilizes one-dimensional convolutions, batch normalization, and max pooling layers is designed to capture the attack patterns in network traffic data. By leveraging the inherent feature extraction capabilities of CNNs, the proposed architecture demonstrates improved performance in identifying network intrusions compared to traditional ML models. The effectiveness of the novel …

Uav-Based Transport Management For Smart Cities Using Machine Learning, Sweekruthi Balivada, Jerry Gao, Yuting Sha, Manisha Lagisetty, Damini Vichare

Faculty Research, Scholarly, and Creative Activity

Highlights: What are the main findings? A smart transport management system based on UAV data integrating advanced machine learning and deep learning techniques is proposed to enhance road anomaly detection and severity classification. The system employs a comprehensive multi-stage framework, integrating a high-precision obstacle detection model, six specialized severity classification models, and an aggregation model to deliver accurate anomaly assessment, enabling strategic, data-driven road maintenance and enhanced transportation safety. What is the implication of the main finding? A scalable and efficient solution is proposed to enhance road safety and optimize transportation management through intelligent anomaly detection and severity assessment. This …