Open Access. Powered by Scholars. Published by Universities.®
- Institution
-
- Universitas Negeri Malang (106)
- Old Dominion University (57)
- Embry-Riddle Aeronautical University (27)
- Air Force Institute of Technology (26)
- University of Nebraska - Lincoln (15)
-
- Southern Methodist University (13)
- University of Arkansas, Fayetteville (13)
- Clemson University (12)
- City University of New York (CUNY) (11)
- Louisiana State University (11)
- West Virginia University (11)
- California Polytechnic State University, San Luis Obispo (10)
- New Jersey Institute of Technology (9)
- Case Western Reserve University (8)
- Purdue University (8)
- Binghamton University (7)
- Chapman University (7)
- Michigan Technological University (7)
- Washington University in St. Louis (7)
- Chinese Academy of Sciences (6)
- Tsinghua University Press (6)
- University of Kentucky (6)
- University of Louisville (6)
- DePaul University (5)
- Georgia Southern University (5)
- Kennesaw State University (5)
- LSU New Orleans (5)
- Mississippi State University (5)
- Technological University Dublin (5)
- The Texas Medical Center Library (5)
- Keyword
-
- Machine Learning (41)
- Machine learning (41)
- Deep learning (25)
- Deep Learning (21)
- Artificial Intelligence (14)
-
- Classification (12)
- Artificial intelligence (10)
- Data science (9)
- Natural Language Processing (9)
- Data analysis (8)
- Neural Networks (8)
- AI (7)
- CNN (7)
- Computer vision (7)
- Forecasting (7)
- Convolutional Neural Network (6)
- Data mining (6)
- Remote sensing (6)
- Big data (5)
- Cybersecurity (5)
- Data (5)
- Engineering (5)
- LSTM (5)
- Neural Network (5)
- Neural networks (5)
- Algorithms (4)
- Analysis (4)
- Artificial neural network (4)
- Computer Vision (4)
- Data Science (4)
- Publication Year
- Publication
-
- Knowledge Engineering and Data Science (106)
- Theses and Dissertations (37)
- Electrical & Computer Engineering Faculty Publications (17)
- Dissertations (15)
- Engineering Management & Systems Engineering Faculty Publications (10)
-
- Graduate Theses, Dissertations, and Problem Reports (ETD) (10)
- SMU Data Science Review (10)
- All Dissertations (9)
- Data Science Undergraduate Honors Theses (9)
- Master's Theses (9)
- Math Department Colloquium Series (9)
- Electronic Theses and Dissertations (7)
- McKelvey School of Engineering Graduate Student Theses & Dissertations (7)
- Big Data Mining and Analytics (6)
- Bulletin of Chinese Academy of Sciences (Chinese Version) (6)
- Discovery Day - Daytona Beach (6)
- Dissertations, Master's Theses and Master's Reports (6)
- LSU Doctoral Dissertations (6)
- Northeast Journal of Complex Systems (NEJCS) (6)
- Department of Construction Engineering and Management: Faculty Publications (5)
- Doctoral Dissertations and Master's Theses (5)
- Electrical and Computer Engineering Publications (5)
- Faculty Publications (5)
- LSU Master's Theses (5)
- LSU New Orleans Theses and Dissertations (5)
- Model Desert Terrain Monochromatic DoT Dataset (5)
- Modeling, Simulation and Visualization Student Capstone Conference (5)
- Publications (5)
- Publications and Research (5)
- College of Computing and Digital Media Dissertations (4)
- Publication Type
- File Type
Articles 331 - 360 of 527
Full-Text Articles in Data Science
Developing Positive Thermal Coefficient (Ptc) Heaters For Solar Electric Cooking, Katarina Ivana Brekalo, Andrew Shepherd
Developing Positive Thermal Coefficient (Ptc) Heaters For Solar Electric Cooking, Katarina Ivana Brekalo, Andrew Shepherd
Physics
Positive Thermal Coefficients, PTCs, are materials that abruptly change in resistance in response to changes in temperature. The purpose of this experiment is to explore the viability of using the switching type ceramic PTC thermistor as a replacement for current resistive heaters. These types of PTCs have a nonlinear change in resistance with increases in temperature. This device will be used as a temperature-controlling heating element intended to power an Insulated Solar Electric Cooker (ISEC). The ISEC is designed to cook meals throughout the day for impacted communities as an alternative cooking method that doesn’t require biofuel as an energy …
Automated Identification Of Astronauts On Board The International Space Station: A Case Study In Space Archaeology, Rao Hamza Ali, Amir Kanan Kashefi, Alice C. Gorman, Justin St. P. Walsh, Erik J. Linstead
Automated Identification Of Astronauts On Board The International Space Station: A Case Study In Space Archaeology, Rao Hamza Ali, Amir Kanan Kashefi, Alice C. Gorman, Justin St. P. Walsh, Erik J. Linstead
Art Faculty Articles and Research
We develop and apply a deep learning-based computer vision pipeline to automatically identify crew members in archival photographic imagery taken on-board the International Space Station. Our approach is able to quickly tag thousands of images from public and private photo repositories without human supervision with high degrees of accuracy, including photographs where crew faces are partially obscured. Using the results of our pipeline, we carry out a large-scale network analysis of the crew, using the imagery data to provide novel insights into the social interactions among crew during their missions.
Three Case Studies Of Using Hybrid Model Machine Learning Techniques In Educational Data Mining To Improve The Classification Accuracies, Sujan Poudyal
Theses and Dissertations
A multitude of data is being produced from the increase in instructional technology, e-learning resources, and online courses. This data could be used by educators to analyze and extract useful information which could be beneficial to both instructors and students. Educational Data Mining (EDM) extracts hidden information from data contained within the educational domain. In data mining, hybrid method is the combination of various machine learning techniques. Through this dissertation, the novel use of machine learning hybrid techniques was explored in EDM using three educational case studies. First, in consideration for the importance of students’ attention, on and off-task data …
Ensemble Tree-Based Machine Learning For Imaging Data, Reza Iranzad
Ensemble Tree-Based Machine Learning For Imaging Data, Reza Iranzad
Graduate Theses and Dissertations
In particular medical imaging data, such as positron emission tomography (PET), computed tomography (CT), and fluorescence intravital microscopy (IVM), have become prevalent for use in a wide variety of applications, from diagnostic purposes, tracking diseases' progress, and monitoring the effectiveness of treatments to decision-making processes. The detailed information generated by medical imaging has enabled physicians to provide more comprehensive care. Although numerous machine learning algorithms, especially those used for imaging data, have been developed, dealing with unique structures in imaging data remained a big challenge. In this dissertation, we are proposing novel statistical tree-based methods with more efficient and more …
Parallel Algorithms For Scalable Graph Mining: Applications On Big Data And Machine Learning, Naw Safrin Sattar
Parallel Algorithms For Scalable Graph Mining: Applications On Big Data And Machine Learning, Naw Safrin Sattar
LSU New Orleans Theses and Dissertations
Parallel computing plays a crucial role in processing large-scale graph data. Complex network analysis is an exciting area of research for many applications in different scientific domains e.g., sociology, biology, online media, recommendation systems and many more. Graph mining is an area of interest with diverse problems from different domains of our daily life. Due to the advancement of data and computing technologies, graph data is growing at an enormous rate, for example, the number of links in social networks is growing every millisecond. Machine/Deep learning plays a significant role for technological accomplishments to work with big data in modern …
Ocean Wave Prediction And Characterization For Intelligent Maritime Transportation, Pujan Pokhrel
Ocean Wave Prediction And Characterization For Intelligent Maritime Transportation, Pujan Pokhrel
LSU New Orleans Theses and Dissertations
The national Earth System Prediction (ESPC) initiative aims to develop the predictions
for the next generation predictions of atmosphere, ocean, and sea-ice interactions in the scale of days to decades. This dissertation seeks to demonstrate the methods we can use to improve the ESPC models, especially the ocean prediction model. In the application side of the weather forecasts, this dissertation explores imitation learning with constraints to solve combinatorial optimization problems, focusing on the weather routing of surface vessels. Prediction of ocean waves is essential for various purposes, including vessel routing, ocean energy harvesting, agriculture, etc. Since the machine learning approaches …
Computer Aided Diagnosis System For Breast Cancer Using Deep Learning., Asma Baccouche
Computer Aided Diagnosis System For Breast Cancer Using Deep Learning., Asma Baccouche
Electronic Theses and Dissertations
The recent rise of big data technology surrounding the electronic systems and developed toolkits gave birth to new promises for Artificial Intelligence (AI). With the continuous use of data-centric systems and machines in our lives, such as social media, surveys, emails, reports, etc., there is no doubt that data has gained the center of attention by scientists and motivated them to provide more decision-making and operational support systems across multiple domains. With the recent breakthroughs in artificial intelligence, the use of machine learning and deep learning models have achieved remarkable advances in computer vision, ecommerce, cybersecurity, and healthcare. Particularly, numerous …
Optimized Three Deep Learning Models Based-Pso Hyperparameters For Beijing Pm2.5 Prediction, Andri Pranolo, Yingchi Mao, Aji Prasetya Wibawa, Agung Bella Putra Utama, Felix Andika Dwiyanto
Optimized Three Deep Learning Models Based-Pso Hyperparameters For Beijing Pm2.5 Prediction, Andri Pranolo, Yingchi Mao, Aji Prasetya Wibawa, Agung Bella Putra Utama, Felix Andika Dwiyanto
Knowledge Engineering and Data Science
Deep learning is a machine learning approach that produces excellent performance in various applications, including natural language processing, image identification, and forecasting. Deep learning network performance depends on the hyperparameter settings. This research attempts to optimize the deep learning architecture of Long short term memory (LSTM), Convolutional neural network (CNN), and Multilayer perceptron (MLP) for forecasting tasks using Particle swarm optimization (PSO), a swarm intelligence-based metaheuristic optimization methodology: Proposed M-1 (PSO-LSTM), M-2 (PSO-CNN), and M-3 (PSO-MLP). Beijing PM2.5 datasets was analyzed to measure the performance of the proposed models. PM2.5 as a target variable was affected by dew point, pressure, …
Computational Models To Detect Radiation In Urban Environments: An Application Of Signal Processing Techniques And Neural Networks To Radiation Data Analysis, Jose Nicolas Gachancipa
Computational Models To Detect Radiation In Urban Environments: An Application Of Signal Processing Techniques And Neural Networks To Radiation Data Analysis, Jose Nicolas Gachancipa
Beyond: Undergraduate Research Journal
Radioactive sources, such as uranium-235, are nuclides that emit ionizing radiation, and which can be used to build nuclear weapons. In public areas, the presence of a radioactive nuclide can present a risk to the population, and therefore, it is imperative that threats are identified by radiological search and response teams in a timely and effective manner. In urban environments, such as densely populated cities, radioactive sources may be more difficult to detect, since background radiation produced by surrounding objects and structures (e.g., buildings, cars) can hinder the effective detection of unnatural radioactive material. This article presents a computational model …
Non-Gaussian Analysis Of Herbarium Specimen Damageto Optimize Specimen Collection Management, Aris Yaman, Yulia Aris Kartika, Ariani Indrawati, Zaenal Akbar, Lindung P. Manik, Wita Wardani, Tutie Djarwaningsih, Taufik Mahendra, Dadan R. Saleh
Non-Gaussian Analysis Of Herbarium Specimen Damageto Optimize Specimen Collection Management, Aris Yaman, Yulia Aris Kartika, Ariani Indrawati, Zaenal Akbar, Lindung P. Manik, Wita Wardani, Tutie Djarwaningsih, Taufik Mahendra, Dadan R. Saleh
Knowledge Engineering and Data Science
Damage to specimen collections occurs in practically every herbarium across the world. Hence, some precautions must be taken, such as investigating the factors that cause specimen damage in their collections and evaluating their herbarium collection handling and usage policy. However, manual investigation of the causes of herbarium collection damage requires a lot of effort and time. Only a few studies have attempted to investigate the causes of herbarium collection damage. So far, the non-gaussian approach to detecting the causes of damage to herbarium specimens has not been studied before. This study attempted to explore the effect of species type, time, …
Social Distancing Monitoring System Using Deep Learning, Amelia Ritahani Ismail, Nur Shairah Muhd Affendy, Asmarani Ahmad Puzi
Social Distancing Monitoring System Using Deep Learning, Amelia Ritahani Ismail, Nur Shairah Muhd Affendy, Asmarani Ahmad Puzi
Knowledge Engineering and Data Science
COVID-19 has been declared a pandemic in the world by 2020. One way to prevent COVID-19 disease, as the World Health Organization (WHO) suggests, is to keep a distance from other people. It is advised to stay at least 1 meter away from others, even if they do not appear to be sick. The reason is that people can also be the virus carrier without having any symptoms. Thus, many countries have enforced the rules of social distancing in their Standard Operating Procedure (SOP) to prevent the virus spread. Monitoring the social distance is challenging as this requires authorities to …
Automatic 3d Cranial Landmark Positioning Based Onsurface Curvature Feature Using Machine Learning, Putu Hendra Suputra, Anggraini Dwi Sensusiati, Myrtati Dyah Artaria, Gijsbertus Jacob Verkerke, Eko Mulyanto Yuniarno, I Ketut Eddy Purnama
Automatic 3d Cranial Landmark Positioning Based Onsurface Curvature Feature Using Machine Learning, Putu Hendra Suputra, Anggraini Dwi Sensusiati, Myrtati Dyah Artaria, Gijsbertus Jacob Verkerke, Eko Mulyanto Yuniarno, I Ketut Eddy Purnama
Knowledge Engineering and Data Science
Cranial anthropometric reference points (landmarks) play an important role in craniofacial reconstruction and identification. Knowledge to detect the position of landmarks is critical. This work aims to locate landmarks automatically. Landmarks positioning using Surface Curvature Feature (SCF) is inspired by conventional methods of finding landmarks based on morphometrical features. Each cranial landmark has a unique shape. With the appropriate 3D descriptors, the computer can draw associations between shapes and landmarks using machine learning. The challenge in classification and detection in three-dimensional space is to determine the model and data representation. Using three-dimensional raw data in machine learning is a serious …
The Effect Of Resampling On Classifier Performance: Anempirical Study, Utomo Pujianto, Muhammad Iqbal Akbar, Niendhitta Tamia Lassela, Deni Sutaji
The Effect Of Resampling On Classifier Performance: Anempirical Study, Utomo Pujianto, Muhammad Iqbal Akbar, Niendhitta Tamia Lassela, Deni Sutaji
Knowledge Engineering and Data Science
An imbalanced class on a dataset is a common classification problem. The effect of using imbalanced class datasets can cause a decrease in the performance of the classifier. Resampling is one of the solutions to this problem. This study used 100 datasets from 3 websites: UCI Machine Learning, Kaggle, and OpenML. Each dataset will go through 3 processing stages: the resampling process, the classification process, and the significance testing process between performance evaluation values of the combination of classifier and the resampling using paired t-test. The resampling used in the process is Random Undersampling, Random Oversampling, and SMOTE. The classifier …
A Comparison Of Machine Learning Models To Prioritise Emailsusing Emotion Analysis For Customer Service Excellence, Mohammad Yasser Chuttur, Yashinee Parianen
A Comparison Of Machine Learning Models To Prioritise Emailsusing Emotion Analysis For Customer Service Excellence, Mohammad Yasser Chuttur, Yashinee Parianen
Knowledge Engineering and Data Science
There has been little research on machine learning for email prioritization for customer service excellence. To fill this gap, we propose and assess the efficacy of various machine learning techniques for classifying emails into three degrees of priority: high, low, and neutral, based on the emotions inherent in the email content. It is predicted that after emails are classified into those three categories, recipients will be able to respond to emails more efficiently and provide better customer service. We use the NRC Emotion Lexicon to construct a labeled email dataset of 517,401 messages for our proposal. Following that, we train …
Fish Image Classification Using Transfer Learning Method Withadaptive Learning Rate, Rizka Suhana, Wayan Firdaus Mahmudy, Agung Setia Budi
Fish Image Classification Using Transfer Learning Method Withadaptive Learning Rate, Rizka Suhana, Wayan Firdaus Mahmudy, Agung Setia Budi
Knowledge Engineering and Data Science
The diversity of fish species in coral reef ecosystems is one of the indications in determining health in coral reef ecosystems. Many Indonesian Fisheries and Marine Research and Development Agency experts carefully classify fish images. A reliable technique for performing image classification is Convolutional Neural Network (CNN). Transfer learning appears and adopts part of CNN, namely the modified convolution layer. The paper aims to solve the fish classification problem using the pre-trained model of Mobilenet V2. The model has a low computational process and does not use too many memory resources when training image data. The research image data used …
Human Facial Expressions Identification Using Convolutionalneural Network With Vgg16 Architecture, Luther Alexander Latumakulita, Sandy Laurentius Lumintang, Deiby Tineke Salaki, Steven R. Sentinuwo, Alwin Melkie Sambul, Noorul Islam
Human Facial Expressions Identification Using Convolutionalneural Network With Vgg16 Architecture, Luther Alexander Latumakulita, Sandy Laurentius Lumintang, Deiby Tineke Salaki, Steven R. Sentinuwo, Alwin Melkie Sambul, Noorul Islam
Knowledge Engineering and Data Science
The human facial expression identification system is essential in developing human interaction and technology. The development of Artificial Intelligence for monitoring human emotions can be helpful in the workplace. Commonly, there are six basic human expressions, namely anger, disgust, fear, happiness, sadness, and surprise, that the system can identify. This study aims to create a facial expression identification system based on basic human expressions using the Convolutional Neural Network (CNN) with a 16-layer VGG architecture. Two thousand one hundred thirty-seven facial expression images were selected from the FER2013, JAFFE, and MUG datasets. By implementing image augmentation and setting up the …
Sentiment Analysis Of Amazon Product Reviews Usingsupervised Machine Learning Techniques, Naveed Sultan
Sentiment Analysis Of Amazon Product Reviews Usingsupervised Machine Learning Techniques, Naveed Sultan
Knowledge Engineering and Data Science
Today, everything is sold online, and many individuals can post reviews about different products to show feedback. Serves as feedback for businesses regarding buyer reviews, performance, product quality, and seller service. The project focuses on buyer opinions based on Mobile Phone reviews. Sentiment analysis is the function of analyzing all these data, obtaining opinions about these products and services that classify them as positive, negative, or neutral. This insight can help companies improve their products and help potential buyers make the right decisions. Once the preprocessing is classified on a trained dataset, these reviews must be preprocessed to remove unwanted …
Automatic Contact Tracing Using Bluetooth Low Energy Signals And Imu Sensor Readings, Suriyadeepan Ramamoorthy, Joyce Mahon, Michael O'Mahony, Jean Francois Itangayenda, Tendai Mukande, Tlamelo Makati
Automatic Contact Tracing Using Bluetooth Low Energy Signals And Imu Sensor Readings, Suriyadeepan Ramamoorthy, Joyce Mahon, Michael O'Mahony, Jean Francois Itangayenda, Tendai Mukande, Tlamelo Makati
Other resources
In this report, we present our solution to the challenge provided by the SFI Centre for Machine Learning (ML-Labs) in which the distance between two phones needs to be estimated. It is a modified version of the NIST Too Close For Too Long (TC4TL) Challenge, as the time aspect is excluded. We propose a feature-based approach based on Bluetooth RSSI and IMU sensory data, that outperforms the previous state of the art by a significant margin, reducing the error down to 0.071. We perform an ablation study of our model that reveals interesting insights about the relationship between the distance …
Un-Fair Trojan: Targeted Backdoor Attacks Against Model Fairness, Nicholas Furth
Un-Fair Trojan: Targeted Backdoor Attacks Against Model Fairness, Nicholas Furth
Theses
Machine learning models have been shown to be vulnerable against various backdoor and data poisoning attacks that adversely affect model behavior. Additionally, these attacks have been shown to make unfair predictions with respect to certain protected features. In federated learning, multiple local models contribute to a single global model communicating only using local gradients, the issue of attacks become more prevalent and complex. Previously published works revolve around solving these issues both individually and jointly. However, there has been little study on the effects of attacks against model fairness. Demonstrated in this work, a flexible attack, which we call Un-Fair …
Text Summarization Towards Scientific Information Extraction, Abigail Keller
Text Summarization Towards Scientific Information Extraction, Abigail Keller
College of Computing and Digital Media Dissertations
Despite the exponential growth in scientific textual content, research publications are still the primary means for disseminating vital discoveries to experts within their respective fields. These texts are predominantly written for human consumption resulting in two primary challenges; experts cannot efficiently remain well-informed to leverage the latest discoveries, and applications that rely on valuable insights buried in these texts cannot effectively build upon published results. As a result, scientific progress stalls. Automatic Text Summarization (ATS) and Information Extraction (IE) are two essential fields that address this problem. While the two research topics are often studied independently, this work proposes to …
Impact Of Climate Oscillations/Indices On Hydrological Variables In The Mississippi River Valley Alluvial Aquifer., Meena Raju
Theses and Dissertations
The Mississippi River Valley Alluvial Aquifer (MRVAA) is one of the most productive agricultural regions in the United States. The main objectives of this research are to identify long term trends and change points in hydrological variables (streamflow and rainfall), to assess the relationship between hydrological variables, and to evaluate the influence of global climate indices on hydrological variables. Non-parametric tests, MMK and Pettitt’s tests were used to analyze trend and change points. PCC and Streamflow elasticity analysis were used to analyze the relationship between streamflow and rainfall and the sensitivity of streamflow to rainfall changes. PCC and MLR analysis …
How Blockchain Solutions Enable Better Decision Making Through Blockchain Analytics, Sammy Ter Haar
How Blockchain Solutions Enable Better Decision Making Through Blockchain Analytics, Sammy Ter Haar
Information Systems Undergraduate Honors Theses
Since the founding of computers, data scientists have been able to engineer devices that increase individuals’ opportunities to communicate with each other. In the 1990s, the internet took over with many people not understanding its utility. Flash forward 30 years, and we cannot live without our connection to the internet. The internet of information is what we called early adopters with individuals posting blogs for others to read, this was known as Web 1.0. As we progress, platforms became social allowing individuals in different areas to communicate and engage with each other, this was known as Web 2.0. As Dr. …
Nucleate Boiling Under Different Gravity Values: Numerical Simulations & Data-Driven Techniques., Sandipan Banerjee
Nucleate Boiling Under Different Gravity Values: Numerical Simulations & Data-Driven Techniques., Sandipan Banerjee
Electronic Theses and Dissertations
Nucleate boiling is important in nuclear applications and cooling applications under earth gravity conditions. Under reduced gravity or microgravity environment, it is significant too, especially in space exploration applications. Although multiple studies have been performed on nucleate boiling, the effect of gravity on nucleate boiling is not well understood. This dissertation primarily deals with numerical simulations of nucleate boiling using an adaptive Moment-of-Fluid (MoF) method for a single vapor bubble (water vapor or Perfluoro-n-hexane) in saturated liquid for different gravity levels. Results concerning the growth rate of the bubble, specifically the departure diameter and departure time have been provided. The …
Intra-Hour Solar Forecasting Using Cloud Dynamics Features Extracted From Ground-Based Infrared Sky Images, Guillermo Terrén-Serrano
Intra-Hour Solar Forecasting Using Cloud Dynamics Features Extracted From Ground-Based Infrared Sky Images, Guillermo Terrén-Serrano
Electrical and Computer Engineering ETDs
Due to the increasing use of photovoltaic systems, power grids are vulnerable to the projection of shadows from moving clouds. An intra-hour solar forecast provides power grids with the capability of automatically controlling the dispatch of energy, reducing the additional cost for a guaranteed, reliable supply of energy (i.e., energy storage). This dissertation introduces a novel sky imager consisting of a long-wave radiometric infrared camera and a visible light camera with a fisheye lens. The imager is mounted on a solar tracker to maintain the Sun in the center of the images throughout the day, reducing the scattering effect produced …
Toward Suicidal Ideation Detection With Lexical Network Features And Machine Learning, Ulya Bayram, William Lee, Daniel Santel, Ali Minai, Peggy Clark, Tracy Glauser, John Pestian
Toward Suicidal Ideation Detection With Lexical Network Features And Machine Learning, Ulya Bayram, William Lee, Daniel Santel, Ali Minai, Peggy Clark, Tracy Glauser, John Pestian
Northeast Journal of Complex Systems (NEJCS)
In this study, we introduce a new network feature for detecting suicidal ideation from clinical texts and conduct various additional experiments to enrich the state of knowledge. We evaluate statistical features with and without stopwords, use lexical networks for feature extraction and classification, and compare the results with standard machine learning methods using a logistic classifier, a neural network, and a deep learning method. We utilize three text collections. The first two contain transcriptions of interviews conducted by experts with suicidal (n=161 patients that experienced severe ideation) and control subjects (n=153). The third collection consists of interviews conducted by experts …
Foam-Based Floatovoltaics: A Potential Solution To Disappearing Terminal Natural Lakes, Koami Soulemane Hayibo, Joshua M. Pearce
Foam-Based Floatovoltaics: A Potential Solution To Disappearing Terminal Natural Lakes, Koami Soulemane Hayibo, Joshua M. Pearce
Electrical and Computer Engineering Publications
Terminal lakes are disappearing worldwide because of direct and indirect human activities. Floating photovoltaics (FPV) are a synergistic system with increased energy output because of water cooling, while the FPV reduces water evaporation. This study explores how low-cost foam-based floatovoltaic systems can mitigate the disappearance of natural lakes. A case study is performed on 10%–50% FPV coverage of terminal and disappearing Walker Lake. Water conservation is investigated with a modified Penman-Monteith evapotranspiration method and energy generation is calculated with an operating temperature model experimentally determined from foam-based FPV. Results show FPV saves 52,000,000 m3/year of water and US$6,000,000 at 50% …
Assessing Security Risks With The Internet Of Things, Faith Mosemann
Assessing Security Risks With The Internet Of Things, Faith Mosemann
Senior Honors Theses
For my honors thesis I have decided to study the security risks associated with the Internet of Things (IoT) and possible ways to secure them. I will focus on how corporate, and individuals use IoT devices and the security risks that come with their implementation. In my research, I found out that IoT gadgets tend to go unnoticed as a checkpoint for vulnerability. For example, often personal IoT devices tend to have the default username and password issued from the factory that a hacker could easily find through Google. IoT devices need security just as much as computers or servers …
Quadratic Neural Network Architecture As Evaluated Relative To Conventional Neural Network Architecture, Reid Taylor
Quadratic Neural Network Architecture As Evaluated Relative To Conventional Neural Network Architecture, Reid Taylor
Senior Theses
Current work in the field of deep learning and neural networks revolves around several variations of the same mathematical model for associative learning. These variations, while significant and exceptionally applicable in the real world, fail to push the limits of modern computational prowess. This research does just that: by leveraging high order tensors in place of 2nd order tensors, quadratic neural networks can be developed and can allow for substantially more complex machine learning models which allow for self-interactions of collected and analyzed data. This research shows the theorization and development of mathematical model necessary for such an idea to …
Assessing Photogrammetry Artificial Intelligence In Monumental Buildings’ Crack Digital Detection, Said Maroun, Mostafa Khalifa, Nabil Mohareb
Assessing Photogrammetry Artificial Intelligence In Monumental Buildings’ Crack Digital Detection, Said Maroun, Mostafa Khalifa, Nabil Mohareb
Architecture and Planning Journal (APJ)
Natural and human-made disasters have significant impacts on monumental buildings, threatening them from being deteriorated. If no rapid consolidations took into consideration traumatic accidents would endanger the existence of precious sites. In this context, Beirut's enormous 4th of August 2020 explosion damaged an estimated 640 historical monuments, many volunteers assess damages for more than a year to prevent the more crucial risk of demolitions. This research aims to assist the collaboration ability among photogrammetry science, Artificial Intelligence Model (AIM) and Architectural Coding to optimize the process for better coverage and scientific approach of data specific to the crack disorders to …
Bcse: Blockchain-Based Trusted Service Evaluation Model Over Big Data, Fengyin Li, Xinying Yu, Rui Ge, Yanli Wang, Yang Cui, Huiyu Zhou
Bcse: Blockchain-Based Trusted Service Evaluation Model Over Big Data, Fengyin Li, Xinying Yu, Rui Ge, Yanli Wang, Yang Cui, Huiyu Zhou
Big Data Mining and Analytics
The blockchain, with its key characteristics of decentralization, persistence, anonymity, and auditability, has become a solution to overcome the overdependence and lack of trust for a traditional public key infrastructure on third-party institutions. Because of these characteristics, the blockchain is suitable for solving certain open problems in the service-oriented social network, where the unreliability of submitted reviews of service vendors can cause serious security problems. To solve the unreliability problems of submitted reviews, this paper first proposes a blockchain-based identity authentication scheme and a new trusted service evaluation model by introducing the scheme into a service evaluation model. The new …