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การปลูกและขยายพันธ์ุกล้วยไม้แดงอุบลเพื่อการอนุรักษ์, อภิญญา ไขรัมย์, ภาษิตา ทุ่นศิริ, นิมมานรดี พรหมทอง

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ความยั่งยืนทางทันตกรรม: การศึกษาเชิงเปรียบเทียบระบบทันตกรรมของ Nhs สหราชอาณาจักร กับภาครัฐและเอกชนของไทยในบริบทของระบบสาธารณสุข, น้ำทิพย์ คอนลอน

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เมื่อนกน้อยในกรงทองส่งข่าวถึงผลกระทบของขยะทะเลจากการเกาะติด พันรัด และกักขัง, ศีลาวุธ ดำรงศิริ, ยศวดี ฮะวังจู

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การสร้างพฤติกรรมการคัดแยกขยะอย่างยั่งยืนด้วยแนวทางการจัดการเรียนรู้เชิงรุก (Active Learning) สำหรับนักเรียนชั้นประถมศึกษา กรณีศึกษาโรงเรียนสวนลุมพินี เขตปทุมวัน, รวมพร กะราลัย, ธนวรรณ จันทร์ประเสริฐ, ศศิลักษณ์ ไชยจรัส, อนัญญา พรหมรักษา, วรุต วรรณวัตร, สิตานันท์ พงษ์พุฒ, พัชรีญา ใจภักดี, นุตา ศุภคต

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พื้นผิวภายในอาคาร : เหตุใดจึงสำคัญต่อคุณภาพอากาศภายในอาคาร, มณีรัตน์ องค์วรรณดี, ทับทิม ชาติสุวรรณ์

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เรียนรู้ความพยายามของสิงคโปร์ในการจัดการขยะอย่างยั่งยืน - จากเตาเผาสู่การลดขยะที่ต้นทาง, สุจิตรา วาสนาดำรงดี

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ความท้าทายในการควบคุมการผลิตและใช้พลาสติกที่เติมสารอ๊อกโซ่ (Oxo-Degradable/Oxo-Biodegradable Plastics), ศุภกิจ สุทธิเรืองวงศ์

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ทดสอบกากของเสียปรอทจากเปลือกอะมัลกัมที่ใช้งานแล้ว (Experimental Study Of Mercury Waste From Disposable Amalgam Capsule), วาสินี เกียรติอดิศร, สุรัตน์ มงคลชัยอรัญญา, นันท์มนัส แย้มบุตร

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อนาคตของตลาดสินค้าไร้บรรจุภัณฑ์ในประเทศไทย – ทำอย่างไรถึงจะได้ไปต่อ, พิมพ์พรรณ เดือนแจ่ม

Thai Environment

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Metarag: Identifying Website Owner Using Meta-Path-Guided Dynamic Graph Retrieval-Augmented Generation, Cheng Tu, Yunshan Ma, Bingyang Guo, Qianyu Li, Yang Li, Min Zhang, Fan Shi, Xiang Wang

Research Collection School Of Computing and Information Systems

Website owner identification aims to link websites to their real-world owners, which is crucial for credibility assessment and information provenance in information retrieval and vital for applications in cybersecurity, Internet governance, and digital regulation. Existing approaches for website owner identification primarily rely on querying infrastructure registration records or analyzing webpage content. However, these methods often fail due to incomplete or outdated registration records and sparse webpage content. We observe that inter-website relationships, derived from shared infrastructure data such as primary domains, IP blocks, and geolocations, can provide valuable but underutilized ownership cues. To exploit this insight, we propose MetaRAG, a …

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An Assessment Of Carbon Dioxide Removal Potential From Stormwater Ponds Using Concrete-Based Materials, Yu-Hsuan Tai

Theses and Dissertations (Comprehensive)

Stormwater ponds (SWPs) are engineered stormwater infrastructure known to be major greenhouse gas (GHG) emitters, including carbon dioxide (CO₂). Concrete-based materials have great potential to capture CO₂ as carbonate minerals (CaCO₃), through aqueous carbonation, driven by their alkaline nature and high portlandite (Ca(OH)₂) content. However, the mechanistic understanding of how specific parameters including mass, surface area, and degradation of calcium silicate hydrate (C-S-H) phases collectively control carbonation kinetics and CO₂ uptake under dynamically evolving conditions remains underexplored. Laboratory-scale experiments were conducted to evaluate the effects of cement dosage, particle surface area, and …

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Smart Medical System Integrating Clinical Workflows For Robust Skin Cancer Detection Across Heterogeneous Pathologies, Ahed Abugabah, Prashant Kumar Shukla, Suchi Mishra, Abhishek Dwivedi

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Skin cancer is among the most prevalent and life-threatening dermatological diseases worldwide, with melanoma responsible for a substantial proportion of skin cancer–related deaths due to delayed and unreliable diagnosis. Conventional clinical screening based on visual inspection and expert interpretation is inherently subjective and often affected by inter-observer variability, lesion heterogeneity, and imaging artifacts, highlighting the need for accurate and generalizable automated diagnostic systems. This study proposes a novel hybrid deep learning architecture for skin cancer classification that integrates an attention-guided autoencoder with a transformer-inspired global context modeling module, forming a unified and robust representation learning framework. The encoder–decoder structure is …

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Quantifying Customer Sentiment For Automobile Brand Perception Analysis Using Machine Learning On Twitter, Sujith Samuel Mathew, Kadhim Hayawi, Neethu Venugopal, May El Barachi

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Social networking sites provide a platform for individuals to express their opinions publicly. Brand managers actively use these platforms to gain insights into brand perceptions, as users often share their views on products and services. In this study, we use sentiment analysis to assess customer sentiment towards five leading automobile brands, analyzing text content shared on Twitter(or X). The research models the ’Brand Polarity Score’, which indicates whether customers perceive the brand positively or negatively. This score is further weighted based on the tweet’s influence, characterized by the engagement metrics of the tweet and the author’s follower count. We also …

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A Deep Learning Ensemble Framework For Multi-Subtype Renal Tumor Classification Using Contrast-Enhanced Ct, Hisham Abdeltawab, Ahmed Alksas, Mohamed Ghazal, Ashraf Khalil, Norah Saleh Alghamdi, Rasha T. Abouelkheir, Ahmed Elmahdy, Mohamed Abou El-Ghar, Sohail Contractor, Ayman El–Baz

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Renal cell carcinoma (RCC) is considered the most aggressive and common form of renal cancer. Therefore, early detection is crucial to ensure appropriate and effective treatment planning. In our study, we propose a novel computer-aided diagnostic (CAD) approach which incorporates a deep learning ensemble to differentiate between five renal tumor subtypes, utilising the modality of contrast-enhanced computed tomography (CE-CT). The addressed renal lesions are malignant tumors (chromophobe RCC (chRCC), papillary RCC (pRCC), and clear cell RCC (ccRCC)) and benign tumors (renal oncocytoma (RO) and angiomyolipoma (AML)). Our study includes 280 patients who underwent renal biopsy, 112 patients were diagnosed with …

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Artificial Intelligence (Ai) For Social Innovation In Health Education: Promoting Health Literacy Through Personalized Ai-Driven Learning Tools – A Systematic Review, Dina Mansour Tbaishat, Maha Waleed Elfadel

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Background: Artificial Intelligence (AI) is transforming health education by enabling personalized, adaptive, and scalable approaches that may enhance aspects of health literacy. Despite rapid adoption, comprehensive synthesis of AI tools’ impact on health literacy as social innovation is limited. Understanding these effects guides educators, developers, and policymakers in designing potentially effective, inclusive, and ethical AI interventions. This review examines generative AI models, chatbots, and adaptive learning systems in supporting health literacy globally. Methods: A systematic review was conducted following PRISMA guidelines. Literature was identified primarily through PubMed/Medline, Scopus, and ScienceDirect. Connectedpapers.com was used exclusively as a citation chasing tool, performing …

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Radio Frequency Tagging–Enabled Patient Monitoring: Integrating Mobility Tracking With Early Warning Systems For Enhanced Safety, Ahed Abugabah, Prashant Kumar Shukla, Piyush Kumar Shukla, Abhishek Dwivedi

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Ensuring patient safety in healthcare environments requires continuous monitoring systems capable of identifying early warning signs of clinical risk. Traditional surveillance methods often fail to capture meaningful patterns in patient movement, limiting their ability to prevent incidents such as falls, prolonged immobility, or unnoticed health deterioration. Radio Frequency Tagging technology has been increasingly adopted for real-time patient tracking; however, existing systems are generally limited to location detection and lack predictive insights into patient behaviour. To overcome these limitations, this study presents a Radio Frequency Tagging-based patient monitoring framework that integrates mobility tracking with an early warning mechanism to enable proactive …

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A Systematic Review Of Audio Deepfake Detection Techniques For Digital Investigation, Mahra Alnaqbi, Richard Adeyemi Ikuesan

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Deepfake technology has been driven by advanced machine learning and revolutionized multimedia creation by synthesizing hyper-realistic content. It includes images, videos, and audio. While its creative applications in entertainment and accessibility are significant, the technology also poses critical risks, especially in fraud, disinformation, and identity theft. Audio deepfakes are a subset of this phenomenon that replicate human voices with enhanced precision, mimicking tone, accent, and subtle vocal nuances. This has raised concerns in security-sensitive domains like voice authentication and forensic investigations. This systematic literature review (SLR) adopts PRISMA guidelines to explore the state-of-the-art in audio deepfake detection. It examines existing …

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Understanding Chatbot-Assisted Collaborative Learning Among Female Undergraduate Students, Mohammad Amin Kuhail, Ahmed Shuhaiber, Sinan Salman, Nazik Alturki

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Computer programming can be daunting for beginners due to complex concepts and syntax. Traditional teaching methods, while engaging through gamification and active learning, often lack personalized approaches. Recent advancements in artificial intelligence (AI), particularly large language models (LLMs), present new possibilities for personalized and interactive learning environments. This study introduces a chatbot-assisted collaborative learning environment (CCLE) that leverages an LLM (GPT-4) to enhance collaborative programming education. The CCLE enables real-time guidance and collaboration through natural language interactions, allowing students to work together on programming tasks, edit code collaboratively, and engage with both peers and the educational chatbot. We conducted an …

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A Robust Approach For Olive Leaf Disease Detection In Uncontrolled Environments, Rima Grati, Khouloud Boukadi, Emna Ben Abdallah, Ahmed Seffah

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Detecting diseases in olive leaves is crucial for maintaining tree health and ensuring stable olive production. Early signs of infection often appear on the leaves, making them a key indicator for timely disease detection and intervention. Traditionally, farmers rely on visual inspection or laboratory tests to diagnose plant diseases. However, recent advancements in deep learning (DL) have significantly improved the accuracy and efficiency of olive leaf disease diagnosis. Numerous studies in the literature have explored this task using CNN-based architectures and, more recently, Vision Transformers. While these models have shown promising performance on benchmark datasets, they are often trained and …

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Fig-Gan: Fundus Image Generation Via Deep Learning Based Generative Adversarial Network For Amd Disease Diagnosis, Kailasa Thrishul, Ahed Abugabah, Amina Salhi, Manel Ayadi, Mohamed M. Sithik, D. Jayaprakash, A. Ahilan

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Globally, age-related macular degeneration (AMD) remains a main cause of irreversible vision loss. Recently, deep learning models have primarily focused on classifying fundus images for early detection of AMD progression. However, existing models rarely address the generation of future progression-aware fundus images, particularly when complete real longitudinal follow-up scans are unavailable. This limitation makes it difficult to track retinal changes over time and highlights the need for generative models capable of producing realistic drusen-level structural variations. To address these issues, a novel deep learning-based FIG-GAN model is to generate synthetic future fundus images from baseline inputs. Multi-Attention U-Net (MAU-Net) is …

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Temporally Rigorous And Traceable Predictive Maintenance Via Joint Labeler-Model Optimization, Maytha Al-Ali, Ahmad Alharbi

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Predictive maintenance (PdM) is a critical enabler of intelligent asset management in Industry 4.0, yet many existing frameworks remain difficult to operationalize due to methodological fragmentation. Common limitations include sacrificing temporal realism and class granularity for computational expediency, decoupling labeling strategy design from model hyperparameter optimization, and insufficient support for reproducibility and deployment traceability; particularly in rare-failure regimes. To address these challenges, we propose a unified, end-to-end, and fully traceable PdM framework that jointly optimizes labeling and model parameters while enforcing strict temporal fidelity. The proposed pipeline co-optimizes the failure lookahead window () and LightGBM hyperparameters within a single Bayesian …

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Image And Metadata-Driven Personality Inference For Career Recommendation: A Social Media-Based Ai Framework For Adolescents, Heba Ismail, Maryam Alhefeiti, Ashraf Khalil

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This study presents a novel AI-based framework that leverages Instagram image and metadata analysis to infer Big Five personality traits and deliver personalized career recommendations for high school students in the UAE. Addressing the limitations of traditional recommender systems that rely on self-reported questionnaires or text, the proposed approach uses multimodal visual features—including profile metrics, HSV color patterns, semantic image labels, and texture analysis—to enable a non-intrusive, scalable personalization method. A pilot study involving data from 30 student accounts served as a proof of concept. Correlation analysis identified profile and HSV features as the most predictive, and four machine learning …

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An Advanced Healthcare System With An Automated Vit Model For Dermoscopic Skin Cancer Identification, Ahed Abugabah, Prashant Kumar Shukla, Suchi Mishra, Abhishek Dwivedi

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Early and reliable diagnosis of skin cancer from dermoscopic images remains challenging due to class imbalance, subtle inter-class variations, lesion boundary ambiguity, and illumination inconsistency, which can degrade the robustness of conventional convolutional neural networks (CNNs). To address these limitations, this study proposes an automated smart healthcare framework for dermoscopic skin cancer diagnosis using an Enhanced Vision Transformer (E-ViT) that improves global-context modeling through self-attention while strengthening fine-grained lesion representation learning. Unlike standard ViT configurations, the proposed architecture integrates multi-scale patch embedding and attention refinement to better capture border irregularities and color–texture heterogeneity that are critical for melanoma discrimination. Furthermore, …

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A Performance-Optimized V2v Task Offloading Framework For Real-Time Vehicular Communication, Tariq Qayyum, Asadullah Tariq, Ikbal Taleb, Mohamed Adel Serhani, Zouheir Trabelsi

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As vehicular applications become increasingly complex, their computational demands often exceed the capabilities of individual vehicles. Vehicular Edge Computing (VEC) alleviates this limitation by enabling task delegation to nearby edge resources; however, high mobility, dynamic topology, and fluctuating vehicle density make real-time offloading decisions challenging. To address these issues, we propose a performance-optimized Vehicle-to-Vehicle (V2V) task offloading framework for dense and dynamic Vehicular Ad-hoc Networks (VANETs). The framework follows a two-stage design: (i) context-aware edge-node selection based on live topology capture via periodic beaconing, and (ii) cumulative score-based dynamic priority queuing at the selected edge node. The priority score jointly …

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Large Language Models In Nlp: Evolution, Architectural Trends, And Open Challenges, Haseeb Javed, Babar Shah, Farman Ali, Daehan Kwak

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The rise of Large Language Models (LLMs) has transformed how Natural Language Processing (NLP) and its subdomains are approached. Recent technological advancements have driven this transformation. This study offers researchers a detailed overview of LLMs, comparing them with traditional rule-based systems, statistical techniques, machine learning, neural networks, and the rise of transformer-based architectures. From a wider perspective, language models such as GPT, BERT, T5, PaLM, and LLaMA have facilitated the transformation of entire sectors, including healthcare and business, due to their highly scalable nature. Despite their wide range of applications, LLMs face numerous challenges, such as output biases, limited interpretability, …

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Extending Geometric Acoustic Ray Tracing To Multi-Room Environments: A Case Study On Gunshot Sound Transmission Between Adjacent Rooms, Tyler Ton

Theses and Dissertations

Accurate localization of gunshots in multi-room building environments remains a challenging problem in acoustic forensics and public safety applications. Existing approaches model sound propagation within a single room, neglecting the transmission of acoustic energy through walls and other building materials. This thesis presents a study on modeling multi-room gunshot acoustic transmission, combining geometric ray tracing with structural acoustic transmission-loss modeling to generate impulse responses for two horizontally adjacent rooms separated by a shared wall, providing a foundation for future inter-room gunshot localization work. The proposed system uses GSound-SIR, a geometric acoustics engine, to simulate sound propagation in both of the …

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Computational Studies Of Triplet State Formation In Zinc Dipyrrin Complexes, Reuben Kwabla Adigbli

Electronic Theses and Dissertations

Zinc dipyrromethene complexes are promising earth-abundant photosensitizers due to their strong visible-light absorption and tunable excited states. To rationally design them for photocatalysis and photodynamic therapy, a molecular-level understanding of triplet-state formation is needed, but details of intersystem crossing remain unclear. To study the effect of π-extension, we synthesized an indole-substituted zinc dipyrrin complex: the dipyrromethane ligand was prepared from indole and mesitaldehyde, oxidized to the dipyrromethene, then coordinated with zinc. DFT and TD-DFT calculations determined ground and excited-state geometries and energies in different solvents, modeled absorption spectra, and visualized charge distributions. The results show how solvent polarity shifts energies …

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Generative Ai Adoption And Solvers' Popularity On Supply-Driven Crowdsourcing Platforms: The Dual Role Of Price Signals, Zimeng Zhu, Carol Hsu, Fiona Fui-Hoon Nah, Na Liu

Research Collection School Of Computing and Information Systems

Purpose – We investigate the effect of solvers’ adoption of Generative AI (GenAI) on their popularity in a supply-driven crowdsourcing platform. We also examine the impact of price signals as well as their heterogeneous impact based on the solvers’ membership duration on the platform. Design/methodology/approach – Our analysis focuses on solvers who adopt GenAI for design-related gigs on the supply-driven crowdsourcing platform. By combining propensity score matching (PSM) with multi-period difference-in-differences (DID), we examine how GenAI adoption impacts solvers’ popularity and how price signals affect this main effect. Findings – Our findings reveal that solvers who adopt GenAI tend to …

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Study Of Fast Switching Metastable-State Photoacids And Controlled Drug Release From Polycaprolactone (Pcl) Vascular Scaffold, Rana Salman Abbood Abbood

Theses and Dissertations

Metastable-state photoacid (mPAH) has become a common tool for controlling and driving chemical processes with light. mPAHs with fast reverse reactions are desirable for precise temporal control or generating quick pulses of proton concentration. In this work, different approaches towards fast reversing mPAHs are studied. Experimental and computational results showed that stabilizing the charge–transfer intermediate is an effective way to increase the rate. A novel mPAH with a reverse reaction ≈500 times faster than the most widely used mPAH in methanol has been developed. Another water-soluble mPAH exhibited a reverse reaction with a rate constant of 7.8 s−1, the fastest …

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Suicide Ideation Detection Using Social Media Data And Ensemble Machine Learning Model, Erol Kina, Jin Ghoo Choi, Abid Ishaq, Rahman Shafique, Monica Gracia Villar, Eduardo Silva Alvarado, Isabel De La Torre Diez, Imran Ashraf

Research outputs 2022 to 2026

Identifying the emotional state of individuals has useful applications, particularly to reduce the risk of suicide. Users’ thoughts on social media platforms can be used to find cues on the emotional state of individuals. Clinical approaches to suicide ideation detection primarily rely on evaluation by psychologists, medical experts, etc., which is time-consuming and requires medical expertise. Machine learning approaches have shown potential in automating suicide detection. In this regard, this study presents a soft voting ensemble model (SVEM) by leveraging random forest, logistic regression, and stochastic gradient descent classifiers using soft voting. In addition, for the robust training of SVEM, …

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Full-Text Articles in Entire DC Network

การปลูกและขยายพันธ์ุกล้วยไม้แดงอุบลเพื่อการอนุรักษ์, อภิญญา ไขรัมย์, ภาษิตา ทุ่นศิริ, นิมมานรดี พรหมทอง

Thai Environment

Thai Environment

Thai Environment

Thai Environment

พื้นผิวภายในอาคาร : เหตุใดจึงสำคัญต่อคุณภาพอากาศภายในอาคาร, มณีรัตน์ องค์วรรณดี, ทับทิม ชาติสุวรรณ์

Thai Environment

Thai Environment

ความท้าทายในการควบคุมการผลิตและใช้พลาสติกที่เติมสารอ๊อกโซ่ (Oxo-Degradable/Oxo-Biodegradable Plastics), ศุภกิจ สุทธิเรืองวงศ์

Thai Environment

Thai Environment

อนาคตของตลาดสินค้าไร้บรรจุภัณฑ์ในประเทศไทย – ทำอย่างไรถึงจะได้ไปต่อ, พิมพ์พรรณ เดือนแจ่ม

Thai Environment

Metarag: Identifying Website Owner Using Meta-Path-Guided Dynamic Graph Retrieval-Augmented Generation, Cheng Tu, Yunshan Ma, Bingyang Guo, Qianyu Li, Yang Li, Min Zhang, Fan Shi, Xiang Wang

Research Collection School Of Computing and Information Systems

An Assessment Of Carbon Dioxide Removal Potential From Stormwater Ponds Using Concrete-Based Materials, Yu-Hsuan Tai

Theses and Dissertations (Comprehensive)

Smart Medical System Integrating Clinical Workflows For Robust Skin Cancer Detection Across Heterogeneous Pathologies, Ahed Abugabah, Prashant Kumar Shukla, Suchi Mishra, Abhishek Dwivedi

All Works

Quantifying Customer Sentiment For Automobile Brand Perception Analysis Using Machine Learning On Twitter, Sujith Samuel Mathew, Kadhim Hayawi, Neethu Venugopal, May El Barachi

All Works

A Deep Learning Ensemble Framework For Multi-Subtype Renal Tumor Classification Using Contrast-Enhanced Ct, Hisham Abdeltawab, Ahmed Alksas, Mohamed Ghazal, Ashraf Khalil, Norah Saleh Alghamdi, Rasha T. Abouelkheir, Ahmed Elmahdy, Mohamed Abou El-Ghar, Sohail Contractor, Ayman El–Baz

All Works

Artificial Intelligence (Ai) For Social Innovation In Health Education: Promoting Health Literacy Through Personalized Ai-Driven Learning Tools – A Systematic Review, Dina Mansour Tbaishat, Maha Waleed Elfadel

All Works

Radio Frequency Tagging–Enabled Patient Monitoring: Integrating Mobility Tracking With Early Warning Systems For Enhanced Safety, Ahed Abugabah, Prashant Kumar Shukla, Piyush Kumar Shukla, Abhishek Dwivedi

All Works

A Systematic Review Of Audio Deepfake Detection Techniques For Digital Investigation, Mahra Alnaqbi, Richard Adeyemi Ikuesan

All Works

Understanding Chatbot-Assisted Collaborative Learning Among Female Undergraduate Students, Mohammad Amin Kuhail, Ahmed Shuhaiber, Sinan Salman, Nazik Alturki

All Works

A Robust Approach For Olive Leaf Disease Detection In Uncontrolled Environments, Rima Grati, Khouloud Boukadi, Emna Ben Abdallah, Ahmed Seffah

All Works

Fig-Gan: Fundus Image Generation Via Deep Learning Based Generative Adversarial Network For Amd Disease Diagnosis, Kailasa Thrishul, Ahed Abugabah, Amina Salhi, Manel Ayadi, Mohamed M. Sithik, D. Jayaprakash, A. Ahilan

All Works

Temporally Rigorous And Traceable Predictive Maintenance Via Joint Labeler-Model Optimization, Maytha Al-Ali, Ahmad Alharbi

All Works

Image And Metadata-Driven Personality Inference For Career Recommendation: A Social Media-Based Ai Framework For Adolescents, Heba Ismail, Maryam Alhefeiti, Ashraf Khalil

All Works

An Advanced Healthcare System With An Automated Vit Model For Dermoscopic Skin Cancer Identification, Ahed Abugabah, Prashant Kumar Shukla, Suchi Mishra, Abhishek Dwivedi

All Works

A Performance-Optimized V2v Task Offloading Framework For Real-Time Vehicular Communication, Tariq Qayyum, Asadullah Tariq, Ikbal Taleb, Mohamed Adel Serhani, Zouheir Trabelsi

All Works

Large Language Models In Nlp: Evolution, Architectural Trends, And Open Challenges, Haseeb Javed, Babar Shah, Farman Ali, Daehan Kwak

All Works

Extending Geometric Acoustic Ray Tracing To Multi-Room Environments: A Case Study On Gunshot Sound Transmission Between Adjacent Rooms, Tyler Ton

Theses and Dissertations

Computational Studies Of Triplet State Formation In Zinc Dipyrrin Complexes, Reuben Kwabla Adigbli

Electronic Theses and Dissertations

Generative Ai Adoption And Solvers' Popularity On Supply-Driven Crowdsourcing Platforms: The Dual Role Of Price Signals, Zimeng Zhu, Carol Hsu, Fiona Fui-Hoon Nah, Na Liu

Research Collection School Of Computing and Information Systems

Study Of Fast Switching Metastable-State Photoacids And Controlled Drug Release From Polycaprolactone (Pcl) Vascular Scaffold, Rana Salman Abbood Abbood

Theses and Dissertations

Suicide Ideation Detection Using Social Media Data And Ensemble Machine Learning Model, Erol Kina, Jin Ghoo Choi, Abid Ishaq, Rahman Shafique, Monica Gracia Villar, Eduardo Silva Alvarado, Isabel De La Torre Diez, Imran Ashraf

Research outputs 2022 to 2026