Digital Commons Network^™

Developer Analytics: A Novel Approach To Analyzing Developer Performance Through Football Comparisons, Ahmed Harbaoui, Waseem Ahmed

Journal of King Abdulaziz University: Computing and Information Technology Sciences

This article explores a novel approach to evaluating developer performance and software quality by drawing parallels between programmers and football players. We highlight the inefficiencies in current assessment methods, which often rely on years of experience rather than quantitative metrics that truly reflect a developer's capabilities. By examining the historical evolution of both fields, we identify the significant advancements in performance analysis in football, which are rooted in statistical data. We propose a framework for developer analytics that utilizes performance metrics derived from programming contests, allowing for a more nuanced classification of programmers based on their skills and roles. Our …

Algorithms For Fast Fire Risk Prediction And Real-Time Data Processing, Mirzoyan Mirzaaxmedovich Kamilov, Tolaniddin Ramziddinovich Nurmukhamedov, Oybek Zokirovich Koraboshev, Bakhodir Saydullayevich Achilov

Chemical Technology, Control and Management

Ensuring fire safety in facilities with high fire risk is one of the pressing problems of modern society. Nowadays, there is a great need for accurate and effective prediction systems for fire prevention and rapid response. Since traditional methods do not provide the ability to quickly analyze and predict in real time, the development of algorithms and modern approaches using modern technologies is of great importance. This article analyzes fire risk prediction algorithms, their principles of operation and effectiveness, and considers methods for assessing and predicting fire risk using Artificial Intelligence (AI), Machine Learning (ML), and Big Data technologies. The …

Machine Learning In Baseball Analytics: Sabermetrics And Beyond, Wenbing Zhao, Vyaghri Seetharamayya Akella, Shunkun Yang, Xiong Luo

Electrical and Computer Engineering Faculty Publications

In this article, we provide a comprehensive review of machine learning-based sports analytics in baseball. This review is primarily guided by the following three research questions: (1) What baseball analytics problems have been studied using machine learning? (2) What data repositories have been used? (3) What and how machine learning techniques have been employed for these studies? The findings of these research questions lead to several research contributions. First, we provide a taxonomy for baseball analytics problems. According to the proposed taxonomy, machine learning has been employed to (1) predict individual game plays; (2) determine player performance; (3) estimate player …

The Attitudes And Perspectives Of Laboratory Professionals On The Use Of Machine Learning Combined With Maldi For Viral Identification: A Qualitative Study, Grace Johnson

Honors Projects

The use of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) with machine learning (ML) has been proposed by numerous studies as a novel approach for viral identification. However, the development and implementation of this instrumentation is still in its early stages, and laboratory professionals' perspectives on its feasibility, accuracy, implementation, and effect on current laboratory operating procedures remain underexplored.

This study aimed to investigate laboratory professionals’ attitudes and opinions regarding the use of MALDI-TOF-MS coupled with machine learning for viral identification, focusing on perceived benefits, barriers, and factors that would affect participants’ opinions on implementation.

A qualitative descriptive research …

How Effective Are Machine Learning Algorithms In Enhancing Marketing Strategies?, Kassidy Dunn

Harriet L Wilkes Honors College Theses 2020 - Present

In recent years, machine learning algorithms have become a vital tool in marketing, yet their impact remains underexplored. This study investigates the mechanisms and applications of machine learning algorithms in marketing strategies, assessing whether they benefit or restrict the effectiveness of marketing strategies. Through relevant research and case studies, we argue that while machine learning-driven marketing offers measurable benefits in personalization and return on investment, its implementation must be balanced with ethical considerations to avoid biased outcomes.

Fractal Dimension And Entropy Analysis Of Medical Images For Knn-Based Disease Classification, Krishna Mahapatra, Selvakumar R

Baghdad Science Journal

The study of medical image-based disease detection has witnessed a notable surge in interest and success with computational methods. This work proposes a novel framework to detect diseases at earlier stages from medical images, using mathematical model and Machine Learning. Introduce two new quantitative measures for COVID-19 and Tumor disease detection: image uncertainty based on Shannon entropy and image complexity based on fractal dimension. Our result demonstrated that in COVID-19 positive images exhibited skewed pixel distribution due to the hazy regions resulting in lower entropy values for diseased cases compared to healthy ones. The second quantity, fractal dimension was measured …

Real-Time Active-Learning Method For Audio-Based Anomalous Event Identification And Rare Events Classification For Audio Events Detection, Farkhund Iqbal, Ahmed Abbasi, Ahmad Almadhor, Shtwai Alsubai, Michal Gregus

All Works

Introduction: Audio event detection, the application of scientific methods to analyze audio recordings, can be helpful in examining and analyzing audio recordings to preserve, analyze, and interpret sound evidence. Furthermore, it can be helpful in safety and compliance, security, surveillance, maintenance, and predictive analysis. Audio event detection aims to recover meaningful information from audio recordings, such as determining the authenticity of the recording, identifying the speakers, and reconstructing conversations. However, filtering out noise for better accuracy in audio event detection is a major challenge. A greater sense of public security can be achieved by developing automated event detection systems that …

A Pioneering Artificial Intelligence Tool To Predict Treatment Outcomes In Ovarian Cancer Via Diagnostic Laparoscopy, Xiaotian Ma, Yu-Chun Hsu, Amma Asare, Kai Zhang, Deanna Glassman, Katelyn F Handley, Katherine Foster, Khwahish Sharma, Shannon Westin, Amir Jazaeri, Nicole D Fleming, Pratip K Bhattacharya, Xiaoqian Jiang, Anil K Sood, Shayan Shams

Faculty, Staff and Student Publications

Ovarian cancer is associated with high rates of patient mortality and morbidity. Laparoscopic assessment of tumor localization can be used for treatment planning in newly diagnosed high-grade serous ovarian carcinoma (HGSOC). While spread to multiple intra-abdominal areas is correlated with worse outcomes, whether other morphological tumor differences are also associated with patient outcomes is unknown. Given the large volume of visual information in laparoscopic videos, we investigated whether deep-learning models can capture implicit features and predict treatment outcomes. We developed a novel deep-learning framework using pre-treatment laparoscopic images to assess clinical outcomes following upfront standard treatment, defined as short progression-free …

Measurement Of Groundwater And Contaminant Fluxes In Fractures Using A Combined System Of Passive Flux Meter And Multiport Sampler, Qasim Raza Khan

Thesis/ Dissertation Defenses

Groundwater and contaminant movement in fractured rock aquifers is highly variable. Its dependence on fracture apertures and orientation as well as fracture network interconnectivity is not well understood. This poses a challenge to the measurement of groundwater and contaminant fluxes, especially when using open-hole techniques, which significantly alter natural flow conditions by connecting different fractures along an open borehole or a well. In this work, the use of Fractured Rock Passive Flux Meter (FRPFM) with invisible tracer and visible dye component to measure groundwater fluxes and identify geometric fracture parameters is explored through laboratory experiments. The invisible tracer component results …

Development And Validation Of A Dynamic Real-Time Risk Prediction Model For Intensive Care Units Patients Based On Longitudinal Irregular Data: Multicenter Retrospective Study, Zhuo Zheng, Jiawei Luo, Yingchao Zhu, Lei Du, Lan Lan, Xiaobo Zhou, Xiaoyan Yang, Shixin Huang

Faculty, Staff and Student Publications

Background: Timely and accurate prediction of short-term mortality is critical in intensive care units (ICUs), where patients' conditions change rapidly. Traditional scoring systems, such as the Simplified Acute Physiology Score and Acute Physiology and Chronic Health Evaluation, rely on static variables collected within the first 24 hours of admission and do not account for continuously evolving clinical states. These systems lack real-time adaptability, interpretability, and generalizability. With the increasing availability of high-frequency electronic medical record (EMR) data, machine learning (ML) approaches have emerged as powerful tools to model complex temporal patterns and support dynamic clinical decision-making. However, existing models are …

Using Generative Artificial Intelligence To Improve Software-Defined Network Security: A Brief Survey, Anthony Smith

Honors Theses

This paper aims to explore various approaches to using generative artificial intelligence (GenAI) to improve network security in software-defined networking. While software-defined networks provide a more programmable infrastructure, they are not immune to network security threats. Through a combination of Software-Defined Networking (SDN) technologies and generative AI, it is possible to facilitate improved SDN security approaches that promise enhanced network efficiency and protection. Among these approaches, generative adversarial networks (GAN) based models can be employed to generate adversarial traffic samples to train the proposed AI engines proven to be effective in detecting malicious network traffic. Additionally, generative artificial intelligence can …

Finding Patterns In Disturbance: Modeling Ecosystem Recovery Using A Machine Learning Approach, Paden D. Allsup

Theses and Dissertations

Some of the most visible effects of Anthropogenic climate change are shifts in resource (water) availability, and disturbance regimes across the world. Ecosystems across the world are now being forced to adapt to amplified disturbances, or in some cases, completely new disturbance types. These shifts have major implications for ecosystem stability and functioning, and more broadly for nutrient cycling across the world. In the western USA, wildfire is playing a larger and more dramatic role than before, altering vegetation communities, landscape stability, and soil and stream chemistry. While fire has always played an important role in semi-arid ecosystems, the scale …

Improving Efficiency Of Wind Flow Modeling For Wildfire Applications Using Analytical Parameterization And Neural Networks, Laura Jane Housley

Theses and Dissertations

We model wind velocity fields for applications in wildfire modeling. We improve the accuracy of the mass-conserving WindNinja solver without compromising efficient temporal complexity. We consider two approaches: the parameterization method and the neural network method. In the parameterization method, we analyze eddies in the wake of obstacles by parameterizing cavity zones as inspired by the research by Kaplan & Dinar in 1996. Kaplan & Dinar parameterize cavity zones around rectangular buildings, and we approximate a continuous landscape with rectangular buildings in order to extend this parameterization to complex terrains. In the neural network method, we train a U-net CNN …

Securing Edge-Iiot Networks: A Comprehensive Ensemble-Based Ddos Detection System, Fariba Laiq, Feras Al-Obeidat, Adnan Amin, Fernando Moreira

All Works

As the number of Internet of Things (IoT) devices increases daily due to the rapid growth in technology, every device and network is vulnerable to attacks because it is exposed to the internet. Denial of Service (DoS) is a prevalent type of intrusion on the IoT network in which the server becomes down due to flooding requests. Distributed Denial of Service (DDoS) is a special type of DoS attack where the network of malicious computers called botnet consumes the target's system resources by flooding the requests. Edge computing is closely related to Industrial Internet of Things (IIoT), and industry 4.0. …

Enhancing Animal Shelter Operations With Time Series And Machine Learning, Sakava L. Kiv, Donald L. Anderson, Shivam Negi, Jacquelyn Cheun

SMU Data Science Review

Enhancing animal shelter operations through machine learning involves employing a variety of advanced techniques aimed at increasing efficiency, promoting animal welfare, and optimizing resource allocation. This paper explores predictive analytics for adoption rates using regression models to estimate the likelihood of adoption based on historical data, encompassing variables such as breed, health status, and previous adoption trends. Additionally, classification algorithms are utilized to categorize animals by adoption probability, facilitating better resources and marketing prioritization. Clustering algorithms are employed to group animals according to behavior patterns and/or physical health, enabling tailored medical care and enrichment activities that improve their mental and …

Heavy Ion Testing Of Versal Acap Ai Engines, Caleb S. Price

Theses and Dissertations

With recent developments in artificial intelligence (AI), there has been an ever increasing need for computation power in space. These AI models are traditionally implemented on GPUs. However, these units come at a high cost in terms of weight and power usage. Due to these limitations, many organizations are turning to the Versal ACAP device, a system on chip (SOC) with hundreds of AI engines. This option delivers more computation per watt than a traditional GPU. The desire to use Versal ACAP in space has created a need to perform radiation testing on the device. Extensive testing has been performed …

Distinguishing Multisystem Inflammatory Syndrome In Children From Typhus Using Artificial Intelligence: Mis-C Versus Endemic Typhus (Ai-Met), Angela Chun, Abraham Bautista-Castillo, Isabella Osuna, Kristiana Nasto, Flor M Munoz, Gordon E Schutze, Sridevi Devaraj, Eyal Muscal, Marietta M De Guzman, Kristen Sexson Tejtel, Tiphanie P Vogel, Ioannis A Kakadiaris

Faculty, Staff and Students Publications

Background: The pandemic emergent disease multisystem inflammatory syndrome in children (MIS-C) following coronavirus disease-19 infection can mimic endemic typhus. We aimed to use artificial intelligence (AI) to develop a clinical decision support system that accurately distinguishes MIS-C versus endemic typhus (MET).

Methods: Demographic, clinical, and laboratory features rapidly available following presentation were extracted for 133 patients with MIS-C and 87 patients hospitalized due to typhus. An attention module assigned importance to inputs used to create the 2-phase AI-MET. Phase 1 uses 17 features to arrive at a classification manually (MET-17). If the confidence level is not surpassed, 13 additional features …

Machine Learning To Optimize Use Of Natriuretic Peptides In The Diagnosis Of Acute Heart Failure, Dimitrios Doudesis, Kuan Ken Lee, Mohamed Anwar, Adam J. Singer, Judd E. Hollander, Camille Chenevier-Gobeaux, Yann-Erick Claessens, Desiree Wussler, Dominic Weil, Nikola Kozhuharov, Ivo Strebel, Zaid Sabti, Christopher Defilippi, Stephen Seliger, Evandro Tinoco Mesquita, Jan C. Wiemer, Martin Möckel, Joel Coste, Patrick Jourdain, Komukai Kimiaki, Michihiro Yoshimura, Irwani Ibrahim, Shirley Beng Suat Ooi, Win Sen Kuan, Alfons Gegenhuber, Thomas Mueller, Olivier Hanon, Jean-Sébastien Vidal, Peter Cameron, Louisa Lam, Ben Freedman, Tommy Chung, Sean P. Collins, Christopher J. Lindsell, David E. Newby, Alan G. Japp, Anoop S. V. Shah, Humberto Villacorta, A. Mark Richards, John J V Mcmurray, Christian Mueller, James L. Januzzi, Nicholas L. Mills, Gordon Moe, Carlos Fernando, Hanna K. Gaggin, Antoni Bayes-Genis, Roland Rj Van Kimmenade, Yigal Pinto, Joost H. W. Rutten, Anton H. Van Den Meiracker, Luna Gargani, Nicola R. Pugliese, Pemberton Christopher, Michael Neumaier, Michael Behnes, Ibrahim Akin, Michele Bombelli, Guido Grassi, Peiman Nazerian, Giovanni Albano, Philipp Bahrmann

Department of Emergency Medicine Faculty Papers

AIMS: B-type natriuretic peptide (BNP) and mid-regional pro-atrial natriuretic peptide (MR-proANP) testing are guideline-recommended to aid in the diagnosis of acute heart failure. Nevertheless, the diagnostic performance of these biomarkers is uncertain.

METHODS AND RESULTS: We performed a systematic review and individual patient-level data meta-analysis to evaluate the diagnostic performance of BNP and MR-proANP. We subsequently developed and externally validated a decision-support tool called CoDE-HF that combines natriuretic peptide concentrations with clinical variables using machine learning to report the probability of acute heart failure. Fourteen studies from 12 countries provided individual patient-level data in 8493 patients for BNP and 3899 …

Machine Learning To Optimize Use Of Natriuretic Peptides In The Diagnosis Of Acute Heart Failure, Dimitrios Doudesis, Kuan Ken Lee, Mohamed Anwar, Adam J. Singer, Judd Hollander, Md, Camille Chenevier-Gobeaux, Yann-Erick Claessens, Desiree Wussler, Dominic Weil, Nikola Kozhuharov, Ivo Strebel, Zaid Sabti, Christopher Defilippi, Stephen Seliger, Evandro Tinoco Mesquita, Jan C. Wiemer, Martin Möckel, Joel Coste, Patrick Jourdain, Komukai Kimiaki, Michihiro Yoshimura, Irwani Ibrahim, Shirley Beng Suat Ooi, Win Sen Kuan, Alfons Gegenhuber, Thomas Mueller, Olivier Hanon, Jean-Sébastien Vidal, Peter Cameron, Louisa Lam, Ben Freedman, Tommy Chung, Sean P. Collins, Christopher J Lindsell, David E. Newby, Alan G. Japp, Anoop S V Shah, Humberto Villacorta, A Mark Richards, John J V Mcmurray, Christian Mueller, James L. Januzzi, Nicholas L. Mills

Department of Emergency Medicine Faculty Papers

AIMS: B-type natriuretic peptide (BNP) and mid-regional pro-atrial natriuretic peptide (MR-proANP) testing are guideline-recommended to aid in the diagnosis of acute heart failure. Nevertheless, the diagnostic performance of these biomarkers is uncertain.

METHODS AND RESULTS: We performed a systematic review and individual patient-level data meta-analysis to evaluate the diagnostic performance of BNP and MR-proANP. We subsequently developed and externally validated a decision-support tool called CoDE-HF that combines natriuretic peptide concentrations with clinical variables using machine learning to report the probability of acute heart failure. Fourteen studies from 12 countries provided individual patient-level data in 8493 patients for BNP and 3899 …

Beyond Data Deletion: Addressing Anticompetitive Conduct In The Era Of Machine Learning, Vaibhav Srikaran

Washington Journal of Law, Technology & Arts

Like a hamster wheel, the “virtuous cycle” of data collection, collation, and computation spins to create algorithms to assist modern business operations. Companies accumulate data by the terabyte with hopes that their army of data scientists can make sense of all the noise using machine learning. Potential profits encourage companies to seek out data—sometimes unlawfully through collusion. The use of unlawfully collected data in algorithms harms competition. Such data provides a competitive edge that is not accessible to all marketplace participants. Antitrust laws can police unlawful data sharing, but existing remedies are not effective for restoring competition.

Merely deleting ill-gotten …

Examining Physiological Responses To Misophonic Triggers, Christian O'Reilly, Xuan Yang, Sewon Oh, Doug Wedell, Svetlana Shinkareva

Faculty Publications

We collected and analyzed an array of biosignals (face electromyogram, skin electrodermal activity, peripheral temperature, and electrocardiogram) in 60 participants with and without misophonia, a condition characterized by decreased tolerance to innocuous sounds. Our goal was to objectively characterize the physiological response to misophonia triggering sounds. We found that misophonic responses can be objectively identified in some cases through atypical physiological reactions to triggering stimuli, though not all participants exhibited this response. Our analyses suggest a large interindividual variability in response to misophonic triggers and highlights the need for methodological adjustments in future experiments to increase the detectability of misophonic …

Parameter Estimation Of Binary Black Hole Coalescence Using Lstm Neural Networks, Lane Scheel

Undergraduate Honors Papers

The Advanced Laser Interferometer Gravitational Wave Observatory (aLIGO) made its first detection of gravitational waves in 2015. Since then, the rate of event detection has only increased, with a detection being made every 2-3 days during the current observing run, O4. This rapid influx of data has the potential to create bottle-necks in data analysis efforts, and can delay the scientific progress which require those efforts. Traditional gravitational wave data analysis techniques, such as matched filtering, require extremely large template banks of synthetic gravitational waveforms and can often fail to provide meaningful limits on system parameters. Not only is this …

Monitoring Opioid-Use-Disorder Treatment Adherence Using Smartwatch Gesture Recognition, Andrew Smith, Kuba Jerzmanowski, Phyllis Raynor Phd, Aprn, Pmhnp-Bc, Cynthia F. Corbett Phd, Rn, Faan, Homayoun Valafar

Publications

The opioid epidemic in the United States has significantly impacted pregnant women with opioid use disorder (OUD), leading to increased health and social complications. This study explores the feasibility of using machine learning algorithms with consumer-grade smartwatches to identify medication-taking gestures. The research specifically focuses on treatments for OUD, investigating methadone and buprenorphine taking gestures. Participants (n = 16, all female university students) simulated medication-taking gestures in a controlled lab environment over two weeks, with data collected via Ticwatch E and E3 smartwatches running custom ASPIRE software. The study employed a RegNet-style 1D ResNet model to analyze gesture data, achieving …

Enhancing Cyber Threat Detection In Tweets Using Diffusion Models And Convolutional Neural Networks, Amer Abdulfattah Mohamad Abdulrahman

Theses

This study looks at how two advanced methods, DDPM-v4 and uViT, work with CNN models to find cyber threats from X data. Two datasets were used—one dataset taken from GitHub and another dataset created using the X Developer API. The dataset created had two sizes: small with 500 rows and larger with 2500 rows. The study compares models with diffusion techniques (DDPM-v4 and uViT) and without diffusion techniques. The results show that DDPM-v4 performed the best overall, with the highest accuracy and F1 score. For example, on the X API dataset with 2500 rows, DDPM achieved 98.20% accuracy. The uViT …

On Studying Transformer Networks For Volume-To-Surface Registration Of Inhomogeneous Soft Bodies For Liver Laparoscopy, Michael Adam Young

Theses

An important practical consideration in laparoscopic liver surgery is the limited visual information relative to open surgery. In laparoscopic interventions, the surgeon’s view of the liver surface is generally limited to the scene provided by a single scope with a narrow field-of-view. This limits the ability to navigate towards internal lesions previously identified through pre-procedural imaging. Surgical navigation during laparoscopy could be enhanced by registration of full preoperative liver models derived from pre-procedural imaging scans onto the partial laparoscopic view of the liver. This entails both a rigid registration to match the liver surface view to the preoperative volume, as …

Machine Learning Methods For Hypervelocity Fragment Flyout Characterization, Katharine Larsen

Doctoral Dissertations and Master's Theses

Resulting from breakup events, such as collisions and explosions, hypervelocity fragments create potential hazards for both terrestrial and on-orbit environments, such as terrestrial weapons explosions and satellite breakup events, respectively. To avoid unnecessary damage, an accurate understanding or characterization of hypervelocity fragmentation events is vital. Currently, publicly available two-line elements collected from on-orbit breakup events are limited, excluding pre-detonation parent body conditions, such as orientation, and information of smaller fragments. The uncertainty of these datasets varies between each collected set. Therefore, the overall goal of this work is to employ machine learning to estimate distribution characteristics of a space debris …

Exploring Electrical Impedance Tomography Techniques For Breast Cancer Detection In Three Dimensions, Emily Jean Mader Corcoran

Dissertations (1934 -)

Electrical Impedance Tomography (EIT) is an imaging modality whose reconstruction problem is severely ill-posed; large changes in the interior conductivity can present as small changes in the data. Due to this ill-posedness, reconstructed images generally have low spatial resolution. However, EIT remains a promising area of study for many medical imaging applications, including breast imaging. In this dissertation, a proof of concept study is discussed in Project 1: "Machine Learning for Breast Cancer Detection" wherein machine learning techniques are used to classify breast tumors as malignant or benign from simulated EIT voltage data. Promising results in terms of accuracy and …

Novel Approaches To Analyze Consumer Behavior And Policies To Promote Sustainable Consumption, Bhagyashree Katare, Xuejian Wang, Shuoli Zhao, Jacqueline Yenerall

Agricultural Economics Faculty Publications

Technological advancements, such as online grocery shopping, have significantly transformed consumer retail environments and experiences. Effectively studying consumer behavior in these new environments requires the use of novel methodological approaches, which will also aid in developing interventions to encourage sustainable consumption. This paper begins by providing an overview of the current literature on novel approaches to analyzing consumer behavior, then provides an empirical application by examining consumer decision-making pathways within an online grocery shopping platform. Specifically, the paper focuses on exploring the consumers’ digital footprints, such as page visits, product additions and removals, and interactions with information labels to identify …

An Artificial Intelligence-Based System For Detecting Meta Fake Profiles Via Gradient Boosting And Multilayer Perception, Ruaa A. Al-Falluji, Marwan Ali Albahar, Ahmad Mousa Altamimi

Mesopotamian Journal of CyberSecurity

The rise of metaverse platforms has renewed interest in detecting fake profiles, which pose a significant threat to digital ownership and asset transactions within these virtual environments. If digital ownership is not guaranteed, platforms risk missing the point of the metaverse. Current supervised learning techniques for fake profile detection often struggle to maintain acceptable accuracy and interpretability in practice. To address this problem, this study investigates the application of two machine learning models, multilayer perceptron and gradient boosted trees, for detecting fake profiles, with model evaluation performed via two Explainable AI (XAI) techniques, Local Interpretable Model-agnostic Explanations (LIME) and SHapley …

Enhancing Iot Security Using Lightweight Machine Learning Algorithms: A Comprehensive Approach Using Ensemble Learning, Feature Selection, And Federated Transfer Learning, Khawlah Harahsheh

Electrical & Computer Engineering Theses & Dissertations

The rapid expansion of the Internet of Things (IoT) has introduced significant security vulnerabilities due to the resource-constrained nature of IoT devices and their exposure to cyber threats. Traditional security solutions are often infeasible due to the high computational and storage demands they impose. This dissertation presents a lightweight, AI-driven security framework that enhances IoT network resilience by integrating feature selection, ensemble learning, and federated transfer learning while maintaining data privacy and minimizing computational overhead.

The proposed framework consists of three primary components: Feature Selection for Intrusion Detection, which optimizes performance by reducing redundant data and improving detection accuracy with …