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Advanced Prediction Of Events And Temporal Expressions In Medical Text Using The Jena Api: Integrating Ontologies And Deep Learning, Hafida Tiaiba, Lyazid Sabri, Okba Kazar
Advanced Prediction Of Events And Temporal Expressions In Medical Text Using The Jena Api: Integrating Ontologies And Deep Learning, Hafida Tiaiba, Lyazid Sabri, Okba Kazar
Turkish Journal of Electrical Engineering and Computer Sciences
The automatic recognition of medical concepts and temporal expressions in narrative clinical text enhances the utility of electronic health records (EHRs) and supports clinical decision-making and research. However, challenges arise due to the complexity of medical language, ambiguity of terms, and variability in expression. To address these issues, the use of medical ontologies significantly improves data management in healthcare. A novel approach integrates various medical ontologies covering drugs, symptoms, diseases, anatomy, disease drivers, and food, and with convolutional neural networks (CNNs) -including Standard, Transposed, and Separable convolution models (CONSEPTR)- to extract both medical events (e.g., clinical departments, treatments, problems) and …
Machine Learning Models Approach For The Quantitative Classification Of Ferricyanide Compound Using Electrochemical Detection With Cpe-Fe3o4nps, Süleyman Aşir, Nemah Abu Shama, Najya Maroof Saleem, Devri̇m Kayali, Kami̇l Di̇mi̇li̇ler
Machine Learning Models Approach For The Quantitative Classification Of Ferricyanide Compound Using Electrochemical Detection With Cpe-Fe3o4nps, Süleyman Aşir, Nemah Abu Shama, Najya Maroof Saleem, Devri̇m Kayali, Kami̇l Di̇mi̇li̇ler
Turkish Journal of Electrical Engineering and Computer Sciences
Most common electrochemical analysis techniques used to evaluate enzymes, proteins, and heavy metals over a wide potential range include electrochemical impedance EIS, differential pulse voltammetry DPV, and square wave voltammetry SQWV. Machine leaning algorithms MLA are employed to classify the Potassium ferricyaniyde K3Fe(CN)6 concentrations using a modified carbon paste electrode CPE embedded with iron (II, III) oxide (Fe3O4) NPs. The CV, DPV, and SQWV voltametric data collected from all K3Fe(CN)6 concentrations were used as input data to the machine learning algorithms. Signaling current of K3Fe(CN)6 concentrations improved at Fe3O4 modified with nanoparticles NPs CPE in a comparison with the unmodified …
Balancing Anarchy And Efficiency: Partial Team Formations And Learning In Potential Games, Muhammed Sayin
Balancing Anarchy And Efficiency: Partial Team Formations And Learning In Potential Games, Muhammed Sayin
Turkish Journal of Electrical Engineering and Computer Sciences
Non-cooperative multi-agent learning, focusing on individual rationality (anarchy), often falls short in achieving system-wide efficiency in potential games, a class of games with applications in decentralized control and optimization. On the other hand, cooperative approaches prioritize system efficiency but often via global coordination, which could be impractical, e.g., for large-scale and less controlled environments. To address this dilemma, we propose a novel framework that introduces partial team formations, allowing team members with shared objectives to coordinate their actions while maintaining team-wise rationality for improved system-wide efficiency without the burden of global coordination. We model such interactions as a multi-team game …
A Content-Based Recommender System For The Uav Caching In The Field Of Entertainment In Fog Computing, Elham Darbanian, Mohsen Nickray
A Content-Based Recommender System For The Uav Caching In The Field Of Entertainment In Fog Computing, Elham Darbanian, Mohsen Nickray
Turkish Journal of Electrical Engineering and Computer Sciences
The Unmanned Aerial Vehicle (UAV) can be used as good flying base stations to cache popular content and follow a user mobility pattern, to help them in a suitable services. Conventional edge caching algorithms often prioritize cache contents with higher popularity. Nevertheless, the cache capacity of mobile devices is restricted, and diverse clients may have expansive varieties in content inclination designs. In this manner, the performance and effectiveness of the cache will be so constrained without great strategies. The composition of recommender system and edge caching is considered as a new research topic, which is used to reduce cost and …
Making The Most Of Artificial Intelligence And Large Language Models To Support Collection Development In Health Sciences Libraries, Ivan Portillo, David Carson
Making The Most Of Artificial Intelligence And Large Language Models To Support Collection Development In Health Sciences Libraries, Ivan Portillo, David Carson
Library Articles and Research
This project investigated the potential of generative AI models in aiding health sciences librarians with collection development. Researchers at Chapman University’s Harry and Diane Rinker Health Science campus evaluated four generative AI models—ChatGPT 4.0, Google Gemini, Perplexity, and Microsoft Copilot—over six months starting in March 2024. Two prompts were used: one to generate recent eBook titles in specific health sciences fields and another to identify subject gaps in the existing collection. The first prompt revealed inconsistencies across models, with Copilot and Perplexity providing sources but also inaccuracies. The second prompt yielded more useful results, with all models offering helpful analysis …
"It's Definitely New And Different...It's Really Engaging": Understanding The Power Of Storytelling Towards Secure Password Creation, Rizu Paudel, Mahdi Nasrullah Al-Ameen
"It's Definitely New And Different...It's Really Engaging": Understanding The Power Of Storytelling Towards Secure Password Creation, Rizu Paudel, Mahdi Nasrullah Al-Ameen
Computer Science Student Research
There is a dearth in existing literature to attain systematic understanding of leveraging digital storytelling in security designs. As we begin to address this gap, we focused on user authentication where the existing password composition policies and password meters often fail to help users around creating a strong and memorable password. To this end, we conducted a lab study with 19 participants, where we updated our initial designs in an iterative manner based on their feedback. We then conducted a between-subject online study with 104 participants over Amazon Mechanical Turk to evaluate our designs. We found that all of our …
Securing A Virtual Reality Classroom Using Unity, Okemute Samuel Idonor
Securing A Virtual Reality Classroom Using Unity, Okemute Samuel Idonor
Theses and Dissertations
This study examines how security features might be incorporated into a Unity created virtual reality (VR) classroom, with a particular emphasis on important concerns including user privacy, data security, and illegal access. The increasing usage of virtual reality (VR) technology in educational settings has made it crucial to ensure the security of these immersive systems, especially when it comes to safeguarding sensitive user data. Designing and implementing a secure VR classroom prototype that could successfully protect against common security concerns, such as unauthorized access and data breaches, was the main goal of this project. Multi-Factor Authentication (MFA), Role-Based Access Control …
Performance Evaluation Of Free Space Optical Communication In Dar Es Salaam: Impact Of Scintillation And Modulation Schemes, Mustafa H. Mohsini
Performance Evaluation Of Free Space Optical Communication In Dar Es Salaam: Impact Of Scintillation And Modulation Schemes, Mustafa H. Mohsini
Tanzania Journal of Engineering and Technology (TJET)
Free space optical communication (FSO) holds significant relevance in the modern communication system as it offers high and unlimited data rates, enhanced security, rapid deployment, and low cost for installation. However, the performance of FSO transmission is greatly affected by harsh atmospheric conditions such as wind, temperature, and humidity, which induce scintillation. With the rapid growth of internet users and Dar es Salaam being a business city in Tanzania, higher and unlimited bandwidth for communication is highly demanded. This study primarily aims to evaluate the performance of FSO transmission in Dar es Salaam, Tanzania, by investigating the impact of atmospheric …
Improved Minimum Variance Channel Estimation Techniques For Ofdm Systems, Kwame S. Ibwe
Improved Minimum Variance Channel Estimation Techniques For Ofdm Systems, Kwame S. Ibwe
Tanzania Journal of Engineering and Technology (TJET)
Orthogonal frequency division multiplexing (OFDM) systems face challenges in channel estimation due to noise, variability, and the doubly dispersive nature of wireless channels, which degrade performance. To address these challenges, a multichannel minimum variance double dispersive channel estimator is proposed. The method employs a hybrid approach that combines subspace and minimum variance techniques, optimizing the filter bank output power under a signal-to-noise ratio (SNR) constraint. This design preserves the desired signal while effectively suppressing disturbances, achieving robust performance with reduced computational complexity compared to existing methods. Simulation results demonstrate that the proposed estimator outperforms subspace and asymptotic methods in terms …
Design And Implementation Of Secured Hybrid Gateway Node For Securing Iot - Enabled Distribution Automation, Ally Bitebo
Design And Implementation Of Secured Hybrid Gateway Node For Securing Iot - Enabled Distribution Automation, Ally Bitebo
Tanzania Journal of Engineering and Technology (TJET)
The integration of smart grid and Internet of Things (IoT) technologies plays a crucial role in enhancing the quality of services provided by traditional electrical grids. This combination has enabled the introduction of new services, such as demand response, automatic meter reading, and IoT-enabled Distribution Automation (IoT-DA), which incorporates sensors, actuators, intelligent electrical devices (IEDs), and information and communication technologies to monitor and control the grid. However, this integration also introduces network security risks, including Denial of Service (DoS) attacks, false data injection, and masquerading attacks, such as system node impersonation that can transmit incorrect readings, trigger false alarms, and …
Mathematics In Everyday Life: Exploring Practical Applications And Real-World Impact, Priyant Banerjee, Arshad Bhat
Mathematics In Everyday Life: Exploring Practical Applications And Real-World Impact, Priyant Banerjee, Arshad Bhat
Himalayan Research Papers Archive
Mathematics is an essential part of daily life and influences decisions and problem-solving in various aspects of life. This study explores how mathematical concepts are embedded in daily activities such as financial management, cooking, travel planning, and technological interactions. We will show how arithmetic, algebra, geometry, and statistics are applied in real life to improve decision-making, efficiency, and productivity. Findings indicate that people with higher mathematical literacy solve problems more efficiently, especially in budgeting, as accurate calculations minimize financial mistakes and facilitate long-term financial planning. In cooking, proportional reasoning ensures the accuracy of recipes, thus providing consistent culinary results. Travel …
Effects Of Technical Debt On Software Interoperability, Leonard Peter Binamungu
Effects Of Technical Debt On Software Interoperability, Leonard Peter Binamungu
Tanzania Journal of Engineering and Technology (TJET)
Technical debt (TD) refers to sub-optimal development decisions that make the software costly to maintain and evolve. Examples of TD include structural complexity, violation of coding styles, and code complexity. Existing research has investigated the nature, causes and indicators of TD, as well as tools and strategies for managing TD. However, although TD could hinder the ability of a software system to be interoperable with others, existing literature has limited evidence on how TD affects systems interoperability. This limits the ability of software engineering teams to manage TD in ways that do not hinder systems interoperability. To fill this void, …
Uc-221 Accounting Treasury Industries Web Application, Ibrahima Diallo, Alexandra Baker, Tyler J Hood
Uc-221 Accounting Treasury Industries Web Application, Ibrahima Diallo, Alexandra Baker, Tyler J Hood
C-Day Computing Showcase
This accounting software project is designed to provide a comprehensive and efficient solution for financial management within an organization. By focusing on ease of usability, accuracy, and compliance, the software enables users to record, manage, and analyze accounts, journals, and financial transactions seamlessly. Core features include transaction journalization, chart of accounts setup, financial statement generation, and robust account management, all of which are supported by strong data validation and secure access controls. This system seeks to streamline accounting workflows, minimize human/manual errors, and enhance user experience. The ultimate aim is to deliver an intuitive yet powerful tool that supports effective …
Using Satellite Image Segmentation To Detect Trails, Jeremy Reynolds
Using Satellite Image Segmentation To Detect Trails, Jeremy Reynolds
Theses and Dissertations
This masters thesis proposes an innovative approach to satellite image segmentation by focusing on the detection and mapping of walking, hiking, and biking trails. The motivation behind this project comes from the underexplored area in segmentation techniques for trail identification and offers potential benefits for urban planning, environmental monitoring, and public health. The problem statement addresses the need for a model that can differentiate between various trail types and other natural or man-made elements. The project aims for efficiency and scalability in processing satellite imagery across different compute hardware. The work details several stages: researching existing segmentation techniques, specifically road …
Evaluation Of Green Tea Yoghurt Enriched With Lacticaseibacillus Paracasei E1 Microcapsules On Macrophage M1 Profile In High Fat-Fructose Diet Mice, Esha Ardiansyah, Nur Alfi Maghfirotus Sa’Adah, Rahmi Izati, Belinda Nabiila Al Faizah, Dawama Nur Fadlilah, Septhyanti Aprilia Kavitarna, Mochammad Fitri Atho’Illah, Siti Nur Arifah, Yoga Dwi Jatmiko, Muhaimin Rifa’I
Evaluation Of Green Tea Yoghurt Enriched With Lacticaseibacillus Paracasei E1 Microcapsules On Macrophage M1 Profile In High Fat-Fructose Diet Mice, Esha Ardiansyah, Nur Alfi Maghfirotus Sa’Adah, Rahmi Izati, Belinda Nabiila Al Faizah, Dawama Nur Fadlilah, Septhyanti Aprilia Kavitarna, Mochammad Fitri Atho’Illah, Siti Nur Arifah, Yoga Dwi Jatmiko, Muhaimin Rifa’I
Karbala International Journal of Modern Science
Obesity is caused by an energy imbalance that increases chronic low-grade inflammation, including macrophage cell infiltration. Adipose tissue macrophages are polarized into pro-inflammatory macrophage type 1 (M1), secrete large amounts of pro-inflammatory cytokines, and activate transcription factors. Yoghurt with probiotics is popular at all ages for its health benefits and must be protected by microencapsulation. Green tea (Camellia sinensis L.) fortification provides yoghurt nutrients while improving its functional qualities and bioactivity. This study aimed to evaluate the effect of microencapsulation of Lacticaseibacillus paracasei E1 in green tea yoghurt (GTY) on the profile of M1 macrophages in mice fed a …
Ur-213 Generative Ai & Cybersecurity, Seth G Canada, Shreya Katare
Ur-213 Generative Ai & Cybersecurity, Seth G Canada, Shreya Katare
C-Day Computing Showcase
This research project details the impact of Generative AI on Cybersecurity through both its potential enhancements and threats. Using advanced AI algorithms, this project explores how Generative AI can strengthen cybersecurity through systems like Anomaly Detection, Intrusion Detection Systems (IDS), and Malware Analysis. Also, this project addresses the growing challenges posed from Generative AI. In particular, issues surrounding Deepfake Phishing and Polymorphic Malware are discussed. Solutions to mitigate these issues are also provided to engage further understanding in the field. The goal of this research is to offer practical solutions for addressing the growing field of AI-driven cybersecurity.
Adaptive Workload Management For Enhanced Function Performance In Serverless Computing, Priyanka Ashok Birajdar, V. Harsha, Anurag Satpathy, Sourav Kanti Addya
Adaptive Workload Management For Enhanced Function Performance In Serverless Computing, Priyanka Ashok Birajdar, V. Harsha, Anurag Satpathy, Sourav Kanti Addya
Computer Science Faculty Research & Creative Works
Serverless computing streamlines application deployment by removing the need for infrastructure management, but fluctuating workloads make resource allocation challenging. To solve this, we propose an adaptive workload manager that intelligently balances workloads, optimizes resource use, and adapts to changes with auto-scaling, ensuring efficient and reliable serverless performance. Preliminary experiments demonstrate an ≈ 0.6X% and 2X% improvement in execution time and resource utilization compared to the First-Come-First Serve (FCFS) scheduling algorithm.
Smevca: Stable Matching-Based Ev Charging Assignment In Subscription-Based Models, Arindam Khanda, Anurag Satpathy, Anusha Vangala, Sajal K. Das
Smevca: Stable Matching-Based Ev Charging Assignment In Subscription-Based Models, Arindam Khanda, Anurag Satpathy, Anusha Vangala, Sajal K. Das
Computer Science Faculty Research & Creative Works
The rapid shift from internal combustion engine vehicles to battery-powered electric vehicles (EVs) presents considerable challenges, such as limited charging points (CPs), unpredictable wait times for charging, and difficulty in selecting appropriate CPs for EVs. To address these challenges, we propose a novel end-to-end framework, called Stable Matching based EV Charging Assignment (SMEVCA) that efficiently assigns charge-seeking EVs to CPs with the assistance of roadside units (RSUs). The proposed framework operates within a subscription-based model, ensuring that the subscribed EVs complete their charging within a predefined time limit enforced by a service level agreement (SLA). The framework SMEVCA employs a …
Collision-Free Exploration By Mobile Agents Using Pebbles, Sajal K. Das, Amit Kumar Dhar, Barun Gorain, Madhuri Mahawar
Collision-Free Exploration By Mobile Agents Using Pebbles, Sajal K. Das, Amit Kumar Dhar, Barun Gorain, Madhuri Mahawar
Computer Science Faculty Research & Creative Works
In this paper, we study collision-free graph exploration in an anonymous network. The network is modeled as a graph G = (V, E) where the nodes of the graph are unlabeled, and each edge incident to a node v has a unique label, called the port number, in {0, 1, ⋯, d - 1}, where d is the degree of the node v. Two identical mobile agents, starting from different nodes in G have to explore the nodes of G in such a way that for every node v in G, at least one mobile agent visits v and no …
Elastic Scheduling For Graceful Degradation Of Mixed-Criticality Systems, Zhuoran Sun, Marion Sudvarg, Christopher Gill
Elastic Scheduling For Graceful Degradation Of Mixed-Criticality Systems, Zhuoran Sun, Marion Sudvarg, Christopher Gill
Computer Science Faculty Research & Creative Works
Many mixed-criticality system models drop all jobs of low-criticality tasks when a criticality mode switch occurs, ensuring that high-criticality tasks still can meet their deadlines in the new mode. However, this means that even important low-criticality tasks are discarded, which may not be acceptable in some systems in practice. This paper addresses that distinction between criticality and importance through a new Inelastic Graceful Earliest Deadline First with Virtual Deadlines (IG-EDF-VD) scheme that upon a criticality mode switch only discards the least important low-criticality tasks necessary to ensure feasibility. Moreover, we consider elastic scheduling within our mixed-criticality model (EG-EDF-VD), using compression …
Development And Characterization Of Sodium Alginate-Based Active Edible Films Functionalized With Olive Mill Wastewater Extract, Nassima Hadri, Mohamed Didi Ould El-Hadj, Zineb Mahcene, Fatih Bozkurt, Rusen Metin Yildirim, Youcef Rahmani, Aicha Tedjani, Muhammet Arici
Development And Characterization Of Sodium Alginate-Based Active Edible Films Functionalized With Olive Mill Wastewater Extract, Nassima Hadri, Mohamed Didi Ould El-Hadj, Zineb Mahcene, Fatih Bozkurt, Rusen Metin Yildirim, Youcef Rahmani, Aicha Tedjani, Muhammet Arici
Karbala International Journal of Modern Science
Phenolic compounds from olive mill wastewater (PCO) of Algerian origin were used to produce sodium alginate-based active films using the casting method. The effects of adding various concentrations of PCO (0%, 0.1%, and 0.2% w/v) were evaluated regarding the molecular, morphological, thermal, physicochemical, optical, barrier, biodegradability, antimicrobial, and antioxidant properties of the alginate films. The FTIR and SEM results elucidated the development of a coherent cross-linked structure attributed to hydrogen bonding interactions between PCO and alginate chains. Consequently, the films exhibited enhanced crystallinity and thermal stability, as revealed by DSC analysis. Moreover, PCO addition positively influenced several film properties, including …
Multiparametric Mri Along With Machine Learning Predicts Prognosis And Treatment Response In Pediatric Low-Grade Glioma, Anahita Fathi Kazerooni, Adam Kraya, Komal Rathi, Meen Chul Kim, Arastoo Vossough, Nastaran Khalili, Ariana Familiar, Deep Gandhi, Neda Khalili, Varun Kesherwani, Debanjan Haldar, Hannah Anderson, Run Jin, Aria Mahtabfar, Sina Bagheri, Yiran Guo, Qi Li, Xiaoyan Huang, Yuankun Zhu, Alex Sickler, Matthew R Lueder, Saksham Phul, Mateusz Koptyra, Phillip Storm, Jeffrey Ware, Yuanquan Song, Christos Davatzikos, Jessica Foster, Sabine Mueller, Michael J Fisher, Adam Resnick, Ali Nabavizadeh
Multiparametric Mri Along With Machine Learning Predicts Prognosis And Treatment Response In Pediatric Low-Grade Glioma, Anahita Fathi Kazerooni, Adam Kraya, Komal Rathi, Meen Chul Kim, Arastoo Vossough, Nastaran Khalili, Ariana Familiar, Deep Gandhi, Neda Khalili, Varun Kesherwani, Debanjan Haldar, Hannah Anderson, Run Jin, Aria Mahtabfar, Sina Bagheri, Yiran Guo, Qi Li, Xiaoyan Huang, Yuankun Zhu, Alex Sickler, Matthew R Lueder, Saksham Phul, Mateusz Koptyra, Phillip Storm, Jeffrey Ware, Yuanquan Song, Christos Davatzikos, Jessica Foster, Sabine Mueller, Michael J Fisher, Adam Resnick, Ali Nabavizadeh
Department of Neurosurgery Faculty Papers
Pediatric low-grade gliomas (pLGGs) exhibit heterogeneous prognoses and variable responses to treatment, leading to tumor progression and adverse outcomes in cases where complete resection is unachievable. Early prediction of treatment responsiveness and suitability for immunotherapy has the potential to improve clinical management and outcomes. Here, we present a radiogenomic analysis of pLGGs, integrating MRI and RNA sequencing data. We identify three immunologically distinct clusters, with one group characterized by increased immune activity and poorer prognosis, indicating potential benefit from immunotherapies. We develop a radiomic signature that predicts these immune profiles with over 80% accuracy. Furthermore, our clinicoradiomic model predicts progression-free …
Optimizing Electrode Configurations For Eeg Mild Cognitive Impairment Detection, Yi Jiang, Xin Zhang, Zhiwei Guo, Xiaobo Zhou, Jiayuan He, Ning Jiang
Optimizing Electrode Configurations For Eeg Mild Cognitive Impairment Detection, Yi Jiang, Xin Zhang, Zhiwei Guo, Xiaobo Zhou, Jiayuan He, Ning Jiang
Faculty, Staff and Student Publications
The Optimal electrode configuration of Electroencephalograms (EEG) systems for mild cognitive impairment (MCI) detection and monitoring in non-clinical settings, i.e. number of electrodes and the positions of the electrodes, remains to be explored. In the current study, we explored the optimization of electrode configuration for MCI detection. We used a 32-channel EEG device to record the data of 21 MCI patients and 20 cognitively normal elderly (NC) undergoing working memory (WM) tasks. Based on the differential value (MCI group vs. NC group) from the Power Spectral Density (PSD) value of each electrode in θ and α frequency band during WM …
Yolo-Based Miner Detection Using Thermal Images In Underground Mines, Cyrus Addy, Venkata Sriram Siddhardh Nadendla, Kwame Awuah-Offei
Yolo-Based Miner Detection Using Thermal Images In Underground Mines, Cyrus Addy, Venkata Sriram Siddhardh Nadendla, Kwame Awuah-Offei
Computer Science Faculty Research & Creative Works
Well-designed and effective in-mine robots can expedite miner self-rescue during emergencies and reduce fatalities. These in-mine robots for miner self-rescue can carry out diverse tasks such as scouting (including object detection and autonomous navigation), and payload delivery. However, robots that can effectively detect humans in a dark underground mine do not yet exist. This paper investigates challenges in the design of object detection algorithms for in-mine robots using thermal images, especially to detect people in real-time, in low-light conditions. The research team collected 500 thermal images in the Missouri University of Science & Technology Experimental Mine with the help of …
Scalable And Ethical Insider Threat Detection Through Data Synthesis And Analysis By Llms, Haywood Gelman, John Hastings
Scalable And Ethical Insider Threat Detection Through Data Synthesis And Analysis By Llms, Haywood Gelman, John Hastings
Research & Publications
Insider threats wield an outsized influence on organizations, disproportionate to their small numbers. This is due to the internal access insiders have to systems, information, and infrastructure. Signals for such risks may be found in anonymous submissions to public web-based job search site reviews. This research studies the potential for large language models (LLMs) to analyze and detect insider threat sentiment within job site reviews. Addressing ethical data collection concerns, this research utilizes synthetic data generation using LLMs alongside existing job review datasets. A comparative analysis of sentiment scores generated by LLMs is benchmarked against expert human scoring. Findings reveal …
Using Structural Similarity And Kolmogorov-Arnold Networks For Anatomical Embedding Of Cortical Folding Patterns, Minheng Chen, Chao Cao, Tong Chen, Yan Zhuang, Jing Zhang, Yanjun Lyu, Xiaowei Yu, Lu Zhang, Tianming Liu, Dajiang Zhu
Using Structural Similarity And Kolmogorov-Arnold Networks For Anatomical Embedding Of Cortical Folding Patterns, Minheng Chen, Chao Cao, Tong Chen, Yan Zhuang, Jing Zhang, Yanjun Lyu, Xiaowei Yu, Lu Zhang, Tianming Liu, Dajiang Zhu
Computer Science Faculty Research & Creative Works
The 3-hinge gyrus (3HG) is a newly defined folding pattern, which is the conjunction of gyri coming from three directions in cortical folding. Many studies demonstrated that 3HGs can be reliable nodes when constructing brain networks or connectome since they simultaneously possess commonality and individuality across different individual brains and populations. However, 3HGs are identified and validated within individual spaces, making it difficult to directly serve as the brain network nodes due to the absence of cross-subject correspondence. The 3HG correspondences represent the intrinsic regulation of brain organizational architecture, traditional image-based registration methods tend to fail because individual anatomical properties …
Brain-Adapter: Enhancing Neurological Disorder Analysis With Adapter-Tuning Multimodal Large Language Models, Jing Zhang, Xiaowei Yu, Yanjun Lyu, Lu Zhang, Tong Chen, Chao Cao, Yan Zhuang, Minheng Chen, Tianming Liu, Dajiang Zhu
Brain-Adapter: Enhancing Neurological Disorder Analysis With Adapter-Tuning Multimodal Large Language Models, Jing Zhang, Xiaowei Yu, Yanjun Lyu, Lu Zhang, Tong Chen, Chao Cao, Yan Zhuang, Minheng Chen, Tianming Liu, Dajiang Zhu
Computer Science Faculty Research & Creative Works
Understanding brain disorders is crucial for accurate clinical diagnosis and treatment. Recent advances in Multimodal Large Language Models (MLLMs) offer a promising approach to interpreting medical images with the support of text descriptions. However, previous research has primarily focused on 2D medical images, leaving richer spatial information of 3D images under-explored, and single-modality-based methods are limited by overlooking the critical clinical information contained in other modalities. To address this issue, this paper proposes Brain-Adapter, a novel approach that incorporates an extra bottleneck layer to learn new knowledge and instill it into the original pre-trained knowledge. The major idea is to …
Feature Fusion Transferability Aware Transformer For Unsupervised Domain Adaptation, Xiaowei Yu, Zhe Huang, Zao Zhang
Feature Fusion Transferability Aware Transformer For Unsupervised Domain Adaptation, Xiaowei Yu, Zhe Huang, Zao Zhang
Computer Science Faculty Research & Creative Works
Unsupervised domain adaptation (UDA) aims to leverage the knowledge learned from labeled source domains to improve performance on the unlabeled target domains. While Convolutional Neural Networks (CNNs) have been dominant in previous UDA methods, recent research has shown promise in applying Vision Transformers (ViTs) to this task. In this study, we propose a novel Feature Fusion Transferability Aware Transformer (FFTAT) to enhance ViT performance in UDA tasks. Our method introduces two key innovations: First, we introduce a patch discriminator to evaluate the transferability of patches, generating a transferability matrix. We integrate this matrix into self-attention, directing the model to focus …
Echopulse: Ecg Controlled Echocardiograms Video Generation, Yiwei Li, Sekeun Kim, Zihao Wu, Hanqi Jiang, Yi Pan, Pengfei Jin, Sifan Song, Yucheng Shi, Xiaowei Yu, Tianze Yang, Tianming Liu, Quanzheng Li, Xiang Li
Echopulse: Ecg Controlled Echocardiograms Video Generation, Yiwei Li, Sekeun Kim, Zihao Wu, Hanqi Jiang, Yi Pan, Pengfei Jin, Sifan Song, Yucheng Shi, Xiaowei Yu, Tianze Yang, Tianming Liu, Quanzheng Li, Xiang Li
Computer Science Faculty Research & Creative Works
Echocardiography (ECHO) is essential for cardiac assessments, but its video quality and interpretation heavily rely on manual expertise, leading to inconsistent results from clinical and portable devices. ECHO video generation offers a solution by improving automated monitoring through synthetic data and generating high-quality videos from routine health data. However, existing models often face high computational costs, slow inference, and rely on complex conditional prompts that require experts' annotations. To address these challenges, we propose ECHOPulse, an ECG-conditioned ECHO video generation model. ECHOPulse introduces two key advancements: (1) it accelerates ECHO video generation by leveraging VQ-VAE tokenization and masked visual token …
Exploring The Trade-Offs: Unified Large Language Models Vs Local Fine-Tuned Models For Highly-Specific Radiology Nli Task, Zihao Wu, Lu Zhang, Chao Cao, Xiaowei Yu, Zhengliang Liu, Lin Zhao, Yiwei Li, Haixing Dai, Chong Ma, Gang Li, Wei Liu, Quanzheng Li, Dinggang Shen, Xiang Li, Dajiang Zhu, Tianming Liu
Exploring The Trade-Offs: Unified Large Language Models Vs Local Fine-Tuned Models For Highly-Specific Radiology Nli Task, Zihao Wu, Lu Zhang, Chao Cao, Xiaowei Yu, Zhengliang Liu, Lin Zhao, Yiwei Li, Haixing Dai, Chong Ma, Gang Li, Wei Liu, Quanzheng Li, Dinggang Shen, Xiang Li, Dajiang Zhu, Tianming Liu
Computer Science Faculty Research & Creative Works
Recently, ChatGPT and GPT-4 have emerged and gained immense global attention due to their unparalleled performance in language processing. Despite demonstrating impressive capability in various open-domain tasks, their adequacy in highly specific fields like radiology remains untested. Radiology presents unique linguistic phenomena distinct from open-domain data due to its specificity and complexity. Assessing the performance of large language models (LLMs) in such specific domains is crucial not only for a thorough evaluation of their overall performance but also for providing valuable insights into future model design directions: whether model design should be generic or domain specific. To this end, in …