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Scalable, Pluggable, And Fault Tolerant Multi-Modal Situational Awareness Data Stream Management Systems, Michael Partin Jan 2020

Scalable, Pluggable, And Fault Tolerant Multi-Modal Situational Awareness Data Stream Management Systems, Michael Partin

Browse all Theses and Dissertations

Features and attributes that describe an event (disasters, social movements, etc.) are heterogeneous in nature. For virtually all events that impact humans, technology enables us to capture a large amount and variety of data from many sources, including humans (i.e., social media) and sensors/internet of things (IoTs). The corresponding modalities of data include text, imagery, voice and video, along with structured data such as gazetteers (i.e., location-based data) and government and statistical data. However, even though there is often an abundance of information produced, this information is fragmented across the various modalities and sources. The DisasterRecord system aims to provide …

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Women On Boards Of Philippine Corporations: Quantitative Explorations, Maria C G Bautista, Marlene De Leon, Rudyard Jose R. Nano Iv Jan 2020

Women On Boards Of Philippine Corporations: Quantitative Explorations, Maria C G Bautista, Marlene De Leon, Rudyard Jose R. Nano Iv

Graduate School of Business Publications

This inductive study explored the likelihood and correlates of gender diversity in corporate boards in the Philippines. The improvement of gender diversity on boards is of advocacy and policy interest as the country emerges to middle-high income status. Logistic regression analyses from individuals' (in a directors' talent pool) responses to an online survey showed that females had a likely odds of 0.10 to be on the boards, compared to males. For every one female getting onto boards, 9 would be unable to. Females with advanced degrees were 7x likely to be on boards than female and male counterparts. The odds …

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A Hierarchical Temporal Memory Sequence Classifier For Streaming Data, Jeffrey Barnett Jan 2020

A Hierarchical Temporal Memory Sequence Classifier For Streaming Data, Jeffrey Barnett

CCAC Theses and Dissertations

Real-world data streams often contain concept drift and noise. Additionally, it is often the case that due to their very nature, these real-world data streams also include temporal dependencies between data. Classifying data streams with one or more of these characteristics is exceptionally challenging. Classification of data within data streams is currently the primary focus of research efforts in many fields (i.e., intrusion detection, data mining, machine learning). Hierarchical Temporal Memory (HTM) is a type of sequence memory that exhibits some of the predictive and anomaly detection properties of the neocortex. HTM algorithms conduct training through exposure to a stream …

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Detecting Rogue Manipulation Of Smart Home Device Settings, David Zeichick Jan 2020

Detecting Rogue Manipulation Of Smart Home Device Settings, David Zeichick

CCAC Theses and Dissertations

Smart home devices control a home’s environmental and security settings. This includes devices that control home thermostats, sprinkler systems, light bulbs, and home appliances. Malicious manipulation of the settings of these devices by an outside adversary has caused emotional distress and could even cause physical harm. For example, researchers have reported that there is a rise in domestic abuse perpetrated via smart home devices; victims have reported their thermostat settings being unwittingly manipulated and being locked out of their house due to their smart lock code being changed. Rapid adoption of smart home devices by consumers has led to an …

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Active Learning For Auditory Hierarchy, William Coleman, Sarah Jane Delany, Charlie Cullen, Ming Yan Jan 2020

Active Learning For Auditory Hierarchy, William Coleman, Sarah Jane Delany, Charlie Cullen, Ming Yan

Conference papers

Much audio content today is rendered as a static stereo mix: fundamentally a fixed single entity. Object-based audio envisages the delivery of sound content using a collection of individual sound ‘objects’ controlled by accompanying metadata. This offers potential for audio to be delivered in a dynamic manner providing enhanced audio for consumers. One example of such treatment is the concept of applying varying levels of data compression to sound objects thereby reducing the volume of data to be transmitted in limited bandwidth situations. This application motivates the ability to accurately classify objects in terms of their ‘hierarchy’. That is, whether …

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Towards Machine Learning In Chemical Sensing : Milk Differentiation And Quality Control Through Two-Dimensional Nano-Sensor Array, Yu Sheng Chen Jan 2020

Towards Machine Learning In Chemical Sensing : Milk Differentiation And Quality Control Through Two-Dimensional Nano-Sensor Array, Yu Sheng Chen

Legacy Theses & Dissertations (2009 - 2024)

Herein, we developed a novel artificial tongue using machine learning and 12 nanoassemblies (2D-NAs) to identify and analyzed different kinds of milk beverages for quality control. This biomimetic sensor array was trained to “taste” different milk types as an “artificial tongue” which is the first time we demonstrated that this sensor array can be implemented to complex systems. Two-dimensional nanoparticles (2D-nps) and nine fluorescently labeled single stranded oligonucleotides (ssDNA) with different length and nucleobases were assembled to create 12 2D-NAs. The artificial tongue was deployed to identify and analyze five milk types. All five milk types were discriminated with 95% …

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Uncertainty Learning In Subjective Logic And Pattern Discovery In Network Data, Adilijiang Alimu Jan 2020

Uncertainty Learning In Subjective Logic And Pattern Discovery In Network Data, Adilijiang Alimu

Legacy Theses & Dissertations (2009 - 2024)

Uncertainty caused by unreliable or insufficient data and vulnerable machine learning models

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Lung Cancer Subtype Differentiation From Positron Emission Tomography Images, Oğuzhan Ayyildiz, Zafer Aydin, Bülent Yilmaz, Seyhan Karaçavuş, Kübra Şenkaya, Semra İçer, Erdem Arzu Taşdemi̇r, Eser Kaya Jan 2020

Lung Cancer Subtype Differentiation From Positron Emission Tomography Images, Oğuzhan Ayyildiz, Zafer Aydin, Bülent Yilmaz, Seyhan Karaçavuş, Kübra Şenkaya, Semra İçer, Erdem Arzu Taşdemi̇r, Eser Kaya

Turkish Journal of Electrical Engineering and Computer Sciences

Lung cancer is one of the deadly cancer types, and almost 85 % of lung cancers are nonsmall cell lung cancer (NSCLC). In the present study we investigated classification and feature selection methods for the differentiation of two subtypes of NSCLC, namely adenocarcinoma (ADC) and squamous cell carcinoma (SqCC). The major advances in understanding the effects of therapy agents suggest that future targeted therapies will be increasingly subtype specific. We obtained positron emission tomography (PET) images of 93 patients with NSCLC, 39 of which had ADC while the rest had SqCC. Random walk segmentation was applied to delineate three-dimensional tumor …

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Dynamically Updated Diversified Ensemble-Based Approach For Handling Concept Drift, Kanu Goel, Shalini Batra Jan 2020

Dynamically Updated Diversified Ensemble-Based Approach For Handling Concept Drift, Kanu Goel, Shalini Batra

Turkish Journal of Electrical Engineering and Computer Sciences

Concept drift is the phenomenon where underlying data distribution changes over time unexpectedly. Examining such drifts and getting insight into the executing processes at that instance of time is a big challenge. Prediction models should be capable of handling drifts in scenarios where statistical properties show abrupt changes. Various strategies exist in the literature to deal with such challenging scenarios but the majority of them are limited to the identification of a particular kind of drift pattern. The proposed approach uses online drift detection in a diversified adaptive setting with pruning techniques to formulate a concept drift handling approach, named …

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Short Unsegmented Pcg Classification Based On Ensemble Classifier, Sinam Ajitkumar Singh, Swanirbhar Majumder Jan 2020

Short Unsegmented Pcg Classification Based On Ensemble Classifier, Sinam Ajitkumar Singh, Swanirbhar Majumder

Turkish Journal of Electrical Engineering and Computer Sciences

Diseases associated with the heart are one of the main reasons of death worldwide. Hence, early examination of the heart is important. For analysis of cardiac disorders, a study of heart sounds is a crucial and beneficial approach. Still, automated classification of heart sounds is a challenging task that mainly depends on segmentation of heart sounds and derivation of features using segmented samples. In the literature available for PCG classification provided by PhysioNet/CinC Challenge 2016, most of the research has focused on enhancing the accuracy of the classification model based on complicated segmentation processes and has failed to improve the …

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Deep Neural Network Based M-Learning Model For Predicting Mobile Learners'performance, Muhammad Adnan, Asad Habib, Jawad Ashraf, Shafaq Mussadiq, Arsalan Ali Jan 2020

Deep Neural Network Based M-Learning Model For Predicting Mobile Learners'performance, Muhammad Adnan, Asad Habib, Jawad Ashraf, Shafaq Mussadiq, Arsalan Ali

Turkish Journal of Electrical Engineering and Computer Sciences

The use of deep learning (DL) techniques for mobile learning is an emerging field aimed at developing methods for finding mobile learners' learning behavior and exploring important learning features. The learning features (learning time, learning location, repetition rate, content types, learning performance, learning time duration, and so on) act as fuel to DL algorithms based on which DL algorithms can classify mobile learners into different learning groups. In this study, a powerful and efficient m-learning model is proposed based on DL techniques to model the learning process of m-learners. The proposed m-learning model determines the impact of independent learning features …

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Comparative Analysis Of Classification Techniques For Network Fault Management, Mohammed Madi, Fidaa Jarghon, Yousef Fazea, Omar Almomani, Adeeb Saaidah Jan 2020

Comparative Analysis Of Classification Techniques For Network Fault Management, Mohammed Madi, Fidaa Jarghon, Yousef Fazea, Omar Almomani, Adeeb Saaidah

Turkish Journal of Electrical Engineering and Computer Sciences

Network troubleshooting is a significant process. Many studies were conducted about it. The first step in the troubleshooting procedures is represented in collecting information. It's collected in order to identify the problems. Syslog messages which are sent by almost all network devices include a massive amount of data that concern the network problems. Based on several studies, it was found that analyzing syslog data (which) can be a guideline for network problems and their causes. The detection of network problems can become more efficient if the detected problems have been classified based on the network layers. Classifying syslog data requires …

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A Novel Penalty-Based Wrapper Objective Function For Feature Selection In Big Data Using Cooperative Co-Evolution, A.N.M. Bazlur Rashid, Mohiuddin Ahmed, Leslie F. Sikos, Paul Haskell-Dowland Jan 2020

A Novel Penalty-Based Wrapper Objective Function For Feature Selection In Big Data Using Cooperative Co-Evolution, A.N.M. Bazlur Rashid, Mohiuddin Ahmed, Leslie F. Sikos, Paul Haskell-Dowland

Research outputs 2014 to 2021

The rapid progress of modern technologies generates a massive amount of high-throughput data, called Big Data, which provides opportunities to find new insights using machine learning (ML) algorithms. Big Data consist of many features (also called attributes); however, not all these are necessary or relevant, and they may degrade the performance of ML algorithms. Feature selection (FS) is an essential preprocessing step to reduce the dimensionality of a dataset. Evolutionary algorithms (EAs) are widely used search algorithms for FS. Using classification accuracy as the objective function for FS, EAs, such as the cooperative co-evolutionary algorithm (CCEA), achieve higher accuracy, even …

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Tcad-Machine Learning Framework For Device Variation And Operating Temperature Analysis With Experimental Demonstration, Hiu Yung Wong, Ming Xiao, Boyan Wang, Yan Ka Chiu, Xiaodong Yan, Jiahui Ma, Kohei Sasaki, Han Wang, Yuhao Zhang Jan 2020

Tcad-Machine Learning Framework For Device Variation And Operating Temperature Analysis With Experimental Demonstration, Hiu Yung Wong, Ming Xiao, Boyan Wang, Yan Ka Chiu, Xiaodong Yan, Jiahui Ma, Kohei Sasaki, Han Wang, Yuhao Zhang

Faculty Research, Scholarly, and Creative Activity

This work, for the first time, experimentally demonstrates a TCAD-Machine Learning (TCAD-ML) framework to assist the analysis of device-to-device variation and operating (ambient) temperature without the need of physical quantities extraction. The ML algorithm used in this work is the Principal Component Analysis (PCA) followed by third order polynomial regression. After calibrated to limited 'expensive' experimental data, 'low cost' TCAD simulation is used to generate a large amount of device data to train the ML model. The ML was then used to identify the root cause of device variation and operating temperature from any given experimental current-voltage (I-V) characteristics. We …

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Improvement Of Tcad Augmented Machine Learning Using Autoencoder For Semiconductor Variation Identification And Inverse Design, Kashyap Mehta, Sophia Susan Raju, Ming Xiao, Boyan Wang, Yuhao Zhang, Hiu Yung Wong Jan 2020

Improvement Of Tcad Augmented Machine Learning Using Autoencoder For Semiconductor Variation Identification And Inverse Design, Kashyap Mehta, Sophia Susan Raju, Ming Xiao, Boyan Wang, Yuhao Zhang, Hiu Yung Wong

Faculty Research, Scholarly, and Creative Activity

A machine learning (ML) model by combing two autoencoders and one linear regression model is proposed to avoid overfitting and to improve the accuracy of Technology Computer-Aided Design (TCAD)-augmented ML for semiconductor structural variation identification and inverse design, without using domain expertise. TCAD-augmented ML utilizes TCAD simulations to generate sufficient data for ML model development when experimental data are inadequate. The ML model can then be used to identify semiconductor structural variation for given experimental electrical measurements. In this study, the variation of layer thicknesses in the p-i-n diode is used as a demonstration. An ML model is developed to …

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Estimating Abiotic Thresholds For Sagebrush Condition Class In The Western United States, Stephen Boyte, Bruce K. Wylie, Yingxin Gu, Donald J. Major Jan 2020

Estimating Abiotic Thresholds For Sagebrush Condition Class In The Western United States, Stephen Boyte, Bruce K. Wylie, Yingxin Gu, Donald J. Major

United States Geological Survey: Staff Publications

Sagebrush ecosystems of the western United States can transition from extended periods of relatively stable conditions to rapid ecological change if acute disturbances occur. Areas dominated by native sagebrush can transition from species-rich native systems to altered states where non-native annual grasses dominate, if resistance to annual grasses is low. The non-native annual grasses provide relatively little value to wildlife, livestock, and humans and function as fuel that increases fire frequency. The more land area covered by annual grasses, the higher the potential for fire, thus reducing the potential for native vegetation to reestablish, even when applying restoration treatments. Mapping …

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Causal Effect Random Forest Of Interaction Trees For Learning Individualized Treatment Regimes In Observational Studies: With Applications To Education Study Data, Luo Li Jan 2020

Causal Effect Random Forest Of Interaction Trees For Learning Individualized Treatment Regimes In Observational Studies: With Applications To Education Study Data, Luo Li

CGU Theses & Dissertations

Learning individualized treatment regimes (ITR) using observational data holds great interest in various fields, as treatment recommendations based on individual characteristics may improve individual treatment benefits with a reduced cost. It has long been observed that different individuals may respond to a certain treatment with significant heterogeneity. ITR can be defined as a mapping between individual characteristics to a treatment assignment. The optimal ITR is the treatment assignment that maximizes expected individual treatment effects. Rooted from personalized medicine, many studies and applications of ITR are in medical fields and clinical practice. Heterogeneous responses are also well documented in educational interventions. …

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Improving Pain Management In Patients With Sickle Cell Disease Using Machine Learning Techniques, Fan Yang Jan 2020

Improving Pain Management In Patients With Sickle Cell Disease Using Machine Learning Techniques, Fan Yang

Browse all Theses and Dissertations

Sickle cell disease (SCD) is an inherited red blood cell disorder that can cause a multitude of complications throughout a patient's life. Pain is the most common complication and a significant cause of morbidity. Since pain is a highly subjective experience, both medical providers and patients express difficulty in determining ideal treatment and management strategies for pain. Therefore, the development of objective pain assessment and pain forecasting methods is critical to pain management in SCD. On the other hand, the rapidly increasing use of mobile health (mHealth) technology and wearable devices gives the ability to build a remote health intervention …

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Uncovering Host-Microbiome Interactions In Global Systems With Collaborative Programming: A Novel Approach Integrating Social And Data Sciences [Version 1; Peer Review: Awaiting Peer Review], Jenna Oberstaller, Swamy Rakesh Adapa, Guy Dayhoff Ii, Justin Gibbons, Gregory S. Herbert Jan 2020

Uncovering Host-Microbiome Interactions In Global Systems With Collaborative Programming: A Novel Approach Integrating Social And Data Sciences [Version 1; Peer Review: Awaiting Peer Review], Jenna Oberstaller, Swamy Rakesh Adapa, Guy Dayhoff Ii, Justin Gibbons, Gregory S. Herbert

School of Geosciences Faculty and Staff Publications

Microbiome data are undergoing exponential growth powered by rapid technological advancement. As the scope and depth of microbiome research increases, cross-disciplinary research is urgently needed for interpreting and harnessing the unprecedented data output. However, conventional research settings pose challenges to much-needed interdisciplinary research efforts due to barriers in scientific terminologies, methodology and research-culture. To breach these barriers, our University of South Florida OneHealth Codeathon was designed to be an interactive, hands-on event that solves real-world data problems. The format brought together students, postdocs, faculty, researchers, and clinicians in a uniquely cross-disciplinary, team-focused setting. Teams were formed to encourage equitable distribution …

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Text Mining Methods For Analyzing Online Health Information And Communication, Sifei Han Jan 2020

Text Mining Methods For Analyzing Online Health Information And Communication, Sifei Han

Theses and Dissertations--Computer Science

The Internet provides an alternative way to share health information. Specifically, social network systems such as Twitter, Facebook, Reddit, and disease specific online support forums are increasingly being used to share information on health related topics. This could be in the form of personal health information disclosure to seek suggestions or answering other patients' questions based on their history. This social media uptake gives a new angle to improve the current health communication landscape with consumer generated content from social platforms. With these online modes of communication, health providers can offer more immediate support to the people seeking advice. Non-profit …

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Automated Testing And Bug Reproduction Of Android Apps, Yu Zhao Jan 2020

Automated Testing And Bug Reproduction Of Android Apps, Yu Zhao

Theses and Dissertations--Computer Science

The large demand of mobile devices creates significant concerns about the quality of mobile applications (apps). The corresponding increase in app complexity has made app testing and maintenance activities more challenging. During app development phase, developers need to test the app in order to guarantee its quality before releasing it to the market. During the deployment phase, developers heavily rely on bug reports to reproduce failures reported by users. Because of the rapid releasing cycle of apps and limited human resources, it is difficult for developers to manually construct test cases for testing the apps or diagnose failures from a …

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Methods To Integrate Genetic And Clinical Data For Disease Subtyping, Diana Mabel Diaz-Herrera Jan 2020

Methods To Integrate Genetic And Clinical Data For Disease Subtyping, Diana Mabel Diaz-Herrera

Wayne State University Dissertations

Enormous efforts have been made to collect genetic and clinical data from cancer patients to advance the understanding of disease development and progression. Processing and analyzing these flows of data is challenging. Many computational methods have been proposed to help different fronts of biology and medicine. The integration of clinical and genetic data using computational methods towards personalized medicine is considered the future for oncology studies, and this thesis contributes in this direction. This thesis presents three new data integration approaches to elucidate granular and meaningful disease sub-types from high-dimensional complex genetic and clinical variables, which is an essential step …

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Fourier Transform Infrared Spectroscopy (As A Rapid Method) Coupled With Machine Learning Approaches For Detection And Quantification Of Gluten Contaminations In Grain-Based Foods, Abuchi Godswill Okeke Jan 2020

Fourier Transform Infrared Spectroscopy (As A Rapid Method) Coupled With Machine Learning Approaches For Detection And Quantification Of Gluten Contaminations In Grain-Based Foods, Abuchi Godswill Okeke

Theses and Dissertations--Biosystems and Agricultural Engineering

Cross-contamination between food grains during harvesting, transportation, and/or food processing is still a major issue in the food industry. Due to cross-contact with gluten-rich grains (wheat, barley, and rye grains), gluten can get into food that’s naturally free from gluten and thus may not be safe for consumption for people susceptible to gluten-related disorders such as celiac disease, wheat allergy, gluten intolerance or sensitivity. The conventional method of gluten detection is cumbersome, time-consuming, and requires well-trained personnel. Therefore, there is a need for a rapid and equally effective technique to authenticate gluten contamination in foods. This research work explored the …

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A Review On Multi-Lingual Sentiment Analysis By Machine Learning Methods, Santwana Sagnika, Anshuman Pattanaik, Bhabani Shankar Prasad Mishra, Saroj K. Meher Jan 2020

A Review On Multi-Lingual Sentiment Analysis By Machine Learning Methods, Santwana Sagnika, Anshuman Pattanaik, Bhabani Shankar Prasad Mishra, Saroj K. Meher

Journal Articles

The arrival of e-commerce and the multitude of information presented by the web have established the internet as a principal destination for consumers looking for truthful opinions and multiple viewpoints for some product, news, topic, or trend in the markets. Thus, it is desirable to make this search easier by using systems which sift through the mass of data and summarize the available opinions for easy understanding of the seeker. This task, known as sentiment analysis, is currently a prominent area of research. Sentiment analysis can be useful for businesses, data analysts and data scientists, as well as customers. Even …

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Application Of Machine Learning And Statistical Tools For Ground Motion Selection, Jalal Kiani Jan 2020

Application Of Machine Learning And Statistical Tools For Ground Motion Selection, Jalal Kiani

Electronic Theses and Dissertations Archive

The main objective of the performance-based earthquake engineering (PBEE) framework is to fully understand the scale of the problems due to earthquakes with respect to deaths, dollars, and downtime resulting from the quantification of the risk, which allows prioritization of investment and technical solutions. The key elements of PBEE are seismic hazard analysis, structural analysis, damage analysis, and loss analysis. This dissertation is aimed to implement machine learning, statistical, and probabilistic tools for ground motion (GM) selection that links the seismic hazard, the first element of PBEE, to the structural analysis, the second element of PBEE. The underlying goal is …

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Assessing The Feasibility Of Machine Learning To Predict Chronic Pain In Adolescence, Max A. Kramer Jan 2020

Assessing The Feasibility Of Machine Learning To Predict Chronic Pain In Adolescence, Max A. Kramer

Honors Papers

"Chronic pain affects between 15 to 40% of adolescents worldwide. The impact and prevalence of chronic pain can be felt every day in terms of missed school days, strained familial relationships, and financial stress. While rehabilitation programs specifically designed for chronic pain management exist, they cannot always adapt to the idiosyncratic nature of chronic pain. Machine learning presents a framework to use diary data from individuals in pain and make predictions about the trajectories of their pain and related functioning. This study's goal is to assess the feasibility of using machine learning to predict pain and functioning by constructing, training, …

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Pretraining Deep Learning Models For Natural Language Understanding, Han Shao Jan 2020

Pretraining Deep Learning Models For Natural Language Understanding, Han Shao

Honors Papers

Since the first bidirectional deep learn- ing model for natural language understanding, BERT, emerged in 2018, researchers have started to study and use pretrained bidirectional autoencoding or autoregressive models to solve language problems. In this project, I conducted research to fully understand BERT and XLNet and applied their pretrained models to two language tasks: reading comprehension (RACE) and part-of-speech tagging (The Penn Treebank). After experimenting with those released models, I implemented my own version of ELECTRA, a pretrained text encoder as a discriminator instead of a generator to improve compute-efficiency, with BERT as its underlying architecture. To reduce the number …

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Toward Efficient Automation Of Interpretable Machine Learning Boosting, Nathan Neuhaus Jan 2020

Toward Efficient Automation Of Interpretable Machine Learning Boosting, Nathan Neuhaus

All Master's Theses

Developing efficient automated methods for Interpretable Machine Learning (IML) is an important and long-term goal in the field of Artificial Intelligence. Currently the Machine Learning landscape is dominated by Neural Networks (NNs) and Support Vector Machines (SVMs), models which are often highly accurate. Despite high accuracy, such models are essentially “black boxes” and therefore are too risky for situations like healthcare where real lives are at stake. In such situations, so called “glass-box” models, such as Decision Trees (DTs), Bayesian Networks (BNs), and Logic Relational (LR) models are often preferred, however can succumb to accuracy limitations. Unfortunately, having to choose …

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Efficient Predictive Lossless Hyperspectral Image Compression Using Machine Learning, Zhuocheng Jiang Jan 2020

Efficient Predictive Lossless Hyperspectral Image Compression Using Machine Learning, Zhuocheng Jiang

Dissertations

Hyperspectral imaging technology has found many useful applications in various domains such as remote sensing. Data compression allows for efficient storage and transmission of massive hyperspectral image datasets. In this dissertation, we study efficient predictive coding schemes for lossless compression of hyperspectral images. We use machine learning techniques to improve the following two key components of the predictive coding process: (i) accurate pixel value prediction, and (ii) more efficient entropy coding of the prediction errors (residues). To this end, we propose an adaptive filtering framework based on concatenated neural networks, which are capable of extracting both spatial and spectral correlations …

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Prediction Of Novel Antibiofilm Peptides From Diverse Habitats Using Machine Learning, Bipasa Bose Jan 2020

Prediction Of Novel Antibiofilm Peptides From Diverse Habitats Using Machine Learning, Bipasa Bose

Master's Theses

Multidrug resistant bacteria often lead to biofilm formation. Biofilm is a colonizedform of pathogens (fungi, bacteria) attached to surfaces like animal or plant tissues, medical devices like catheters, and artificial heart valves. Biofilm formation prolongs the survival of microorganisms in an adaptive environment, leading to the spread of infection in different organs and causing a high morbidity rate. Given the rise of chronic infection and antibiotic resistance due to biofilm, it is essential to find an alternative solution to control biofilm infections. Antibiofilm peptides can interact with these biofilm-creating pathogens to inhibit growth, virulence, and biofilm formation. We hypothesized that …

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