Computational Engineering Commons^™

Bibliometric Survey On Effects Of Climate Change On Incidences Of Infectious Diseases, Seema Harshad Patil, Yatharth Jain, Vedant Marathe

Library Philosophy and Practice (e-journal)

^{For understanding the influx of Infectious Diseases, research of climate change and its effects pertaining to the diseases is important. The motive of this bibliometric survey is to understand the research which has been carried out regarding the aforementioned topics. This paper summarizes the research in the 21st Century from 2001 to present. We conducted this analysis using tools such as Gephi, Researchgate, Scopus, ScienceScape, Google Scholar and Mapchart. This Bibliometric Survey on “Effects of Climate Change on Infectious Diseases” showed that maximum publications are articles. These publications are from conferences and journals related to Environmental Science. The United States …}

Combining Bert With Contextual Linguistic Features For Identification Of Propaganda Spans In News Articles, Arjumand Younus, Muhammad Atif Qureshi

Conference papers

Recent endeavours at detection of propaganda in news articles treat this as a fine-grained problem of detecting it within fragments; and hence, transformer based embeddings perform decently in such detection. We build our propaganda detection framework on top of a transformer model simultaneously enriching it with contextual linguistic information of surrounding part-of-speech tags and LIWC categories the word itself belongs to. The evaluation outcomes being encouraging indicate a huge potential for this line of reasoning in natural language processing of news text.

Phonetic Algorithm Performance, Aaron Schneidereit

Senior Honors Projects

AARON SCHNEIDEREIT (Computer Science BS) Phonetic Algorithm Performance Sponsors: Noah Daniels (Computer Science and Statistics) Phonetic Algorithms are used for classifying words based on their pronunciation. These algorithms are used in many text-to-speech technologies and spell-checkers to ensure that a word can be correctly recognized despite minor spelling/pronunciation errors. The process of encoding a word to its phonetic surname is known as Phonetic Matching. Since 1918, there have only been a handful of phonetic algorithms that have been created. The main three algorithms that other phonetic algorithms are built from are Soundex, New York State Identification and Intelligence System (NYSIIS), …

Demystifying Artificial Intelligence Based Digital Twins In Manufacturing- A Bibliometric Analysis Of Trends And Techniques, Satish Kumar, Shruti Patil, Arunkumar Bongale, Ketan Kotecha, Anup Kumar M. Bongale, Pooja Kamat

Library Philosophy and Practice (e-journal)

Nowadays, data is considered as a new life force for operations of physical systems in various domains such as manufacturing, healthcare, transportations, etc. However, the hugely generated data, which mirrors the working essence of the product life cycle, is still underutilised. Digital Twin (DT), a collective representation of active and passive captured data, is a virtual counterpart of the physical resources that could help prevent effective preventive maintenance in any applied domain. Currently, lots of research is going on about the applicability of digital twin in smart IOT based manufacturing industry 4.0 environment. Still, it lacks a formal study, which …

Usage And Awareness Of Cloud Computing Applications By Library Professionals Of Sindh Province, Liaquat Ali Rahoo

Library Philosophy and Practice (e-journal)

The aim of the study is examine the usage and awareness level of cloud computing applications by library professionals of Sindh province. Methods- This study was quantitative survey based. The population of the study was library professionals who are working in different types of libraries likewise academic, special and community libraries of Sindh province. Sampling technique was random simple sample size was 165 library professionals (library assistant, assistant librarian, deputy librarian, librarian. Questionnaire was prepared in google form and distributed by email to selected respondents. Results- The result declared that knowledge and awareness of library professionals regarding cloud …

Interactive Virtual Training: Implementation For Early Career Teachers To Practice Classroom Behavior Management, Alban Delamarre

FIU Electronic Theses and Dissertations

Teachers that are equipped with the skills to manage and prevent disruptive behaviors increase the potential for their students to achieve academically and socially. Student success increases when prevention strategies and effective classroom behavior management (CBM) are implemented in the classroom. However, teachers with less than 5 years of experience, early career teachers (ECTs), are ill equipped to handle disruptive students. ECTs describe disruptive behaviors as a major factor for stress given their limited training in CBM. As a result, disruptive behaviors are reported by ECTs as one of the main reasons for leaving the field.

Virtual training environments (VTEs) …

Adaptive Spline Fitting With Particle Swarm Optimization, Soumya Mohanty, Ethan Fahnestock

Physics and Astronomy Faculty Publications and Presentations

In fitting data with a spline, finding the optimal placement of knots can significantly improve the quality of the fit. However, the challenging high-dimensional and non-convex optimization problem associated with completely free knot placement has been a major roadblock in using this approach. We present a method that uses particle swarm optimization (PSO) combined with model selection to address this challenge. The problem of overfitting due to knot clustering that accompanies free knot placement is mitigated in this method by explicit regularization, resulting in a significantly improved performance on highly noisy data. The principal design choices available in the method …

Modeling And Analyzing Cyber-Physical Systems Using Hybrid Predicate Transition Nets, Dewan Mohammad Moksedul Alam

FIU Electronic Theses and Dissertations

Cyber-Physical Systems (CPSs) are software controlled physical devices that are being used everywhere from utility features in household devices to safety-critical features in cars, trains, aircraft, robots, smart healthcare devices. CPSs have complex hybrid behaviors combining discrete states and continuous states capturing physical laws. Developing reliable CPSs are extremely difficult. Formal modeling methods are especially useful for abstracting and understanding complex systems and detecting and preventing early system design problems. To ensure the dependability of formal models, various analysis techniques, including simulation and reachability analysis, have been proposed in recent decades. This thesis aims to provide a unified formal modeling …

Automatic Delamination Segmentation For Bridge Deck Based On Encoder-Decoder Deep Learning Through Uav-Based Thermography, Chongsheng, Zhexiong Shang, Zhigang Shen

Department of Construction Engineering and Management: Faculty Publications

Concrete deck delamination often demonstrates strong variations in size, shape, and temperature distribution under the influences of outdoor weather conditions. The strong variations create challenges for pure analytical solutions in infrared image segmentation of delaminated areas. The recently developed supervised deep learning approach demonstrated the potentials in achieving automatic segmentation of RGB images. However, its effectiveness in segmenting thermal images remains under-explored. The main challenge lies in the development of specific models and the generation of a large range of labeled infrared images for training. To address this challenge, a customized deep learning model based on encoder-decoder architecture is proposed …

Evaluating Driving Performance Of A Novel Behavior Planning Model On Connected Autonomous Vehicles, Keyur Shah

Honors Scholar Theses

Many current algorithms and approaches in autonomous driving attempt to solve the "trajectory generation" or "trajectory following” problems: given a target behavior (e.g. stay in the current lane at the speed limit or change lane), what trajectory should the vehicle follow, and what inputs should the driving agent apply to the throttle and brake to achieve this trajectory? In this work, we instead focus on the “behavior planning” problem—specifically, should an autonomous vehicle change lane or keep lane given the current state of the system?

In addition, current theory mainly focuses on single-vehicle systems, where vehicles do not communicate with …

Using Case-Level Context To Classify Cancer Pathology Reports, Shang Gao, Mohammed Alawad, Noah Schaefferkoetter, Lynne Penberthy, Xiao-Cheng Wu, Eric B. Durbin, Linda Coyle, Arvind Ramanathan, Georgia Tourassi

Kentucky Cancer Registry Faculty Publications

Individual electronic health records (EHRs) and clinical reports are often part of a larger sequence-for example, a single patient may generate multiple reports over the trajectory of a disease. In applications such as cancer pathology reports, it is necessary not only to extract information from individual reports, but also to capture aggregate information regarding the entire cancer case based off case-level context from all reports in the sequence. In this paper, we introduce a simple modular add-on for capturing case-level context that is designed to be compatible with most existing deep learning architectures for text classification on individual reports. We …

Chaprates, Brinly Xavier, Micole Amanda Marietta, Nidhi Vedantam

Student Scholar Symposium Abstracts and Posters

On the Chapman campus, through taking and choosing various classes, there is a significant need for communication and feedback between students and peers, professors, tutors, and study groups. With this, we wanted to create an application that enables users from various majors to not only easily and effectively communicate with various people in their field, but one that also enables them to give and receive feedback on various classes through a rating system. We believe that the application will aid students in a myriad of specific ways, including being involved in study groups and getting tutoring help, determining which classes …

Performance Optimizations Of Nosql Databases In Distributed Systems, Tristyn Maalouf

Senior Honors Theses

Databases store information about a system and provide a mechanism for data to be accessed and manipulated. While advancements in the 1970s provided a relational database model that has persisted to this day, web-scale era mass data needs surfacing in the 1990s and the early 2000s revealed limitations in the scalability of the relational model. As systems grew and transitioned into distributed architectures to support mass data storage and parallel processing, a complete overhaul of distributed computing technologies evolved that fundamentally departed from the relational data model in favor of the NoSQL data model. The course of this research details …

Sensitivity Analysis Of Data-Driven Groundwater Forecasts To Hydroclimatic Controls In Irrigated Croplands, Alessandro Amaranto, Francesca Pianosi, Dimitri Solomatine, Gerald Corzo-Perez, Francisco Munoz-Arriola

Biological Systems Engineering: Papers and Publications

In the last decades, advancements in computational science have greatly expanded the use of artificial neural networks (ANNs) in hydrogeology, including applications on groundwater forecast, variable selection, extended lead-times, and regime-specific analysis. However, ANN-model performance often omits the sensitivity to ob- servational uncertainties in hydroclimate forcings. The goal of this paper is to implement a data-driven modeling framework for assessing the sensitivity of ANN-based groundwater forecasts to the uncertainties in observational inputs across space, time, and hydrological regimes. The objectives are two-folded. The first objective is to couple an ANN model with the PAWN sensitivity analysis (SA). The second objective …

Perturbed Stress Field Of The Human Lens Capsule After Cataract Surgery, Kurt Ameku, Caleb Berggren, Ryan M. Pedrigi

UCARE Research Products

Current modeling of the human lens capsule has been focused on the mechanism of accommodation and its decline with age, but few studies have modeled the effects of cataract surgery and quantified the altered mechanical environment introduced by the procedure. The goal of this study is to develop the first fully 3-D finite element model of the post-surgical human lens capsule with an implanted device in order to characterize lens capsule mechanics after cataract surgery. The model demonstrates a highly perturbed stress field compared to the native state, which we hypothesize is the primary driving force behind the long-term errant …

Guidelines For Using Streetlight Data For Planning Tasks, Hong Yang, Mecit Cetin, Qingyu Ma

Computational Modeling & Simulation Engineering Faculty Publications

The Virginia Department of Transportation (VDOT) has purchased a subscription to the StreetLight (SL) Data products that mainly offer origin-destination (OD) related metrics through crowdsourcing data. Users can manipulate a data source like this to quickly estimate origin-destination trip tables. Nonetheless, the SL metrics heavily rely on the data points sampled from smartphone applications and global positioning services (GPS) devices, which may be subject to potential bias and coverage issues. In particular, the quality of the SL metrics in relation to meeting the needs of various VDOT work tasks is not clear. Guidelines on the use of the SL metrics …

Deal: Differentially Private Auction For Blockchain Based Microgrids Energy Trading, Muneeb Ul Hassan, Mubashir Husain Rehmani, Jinjun Chen

Publications

Modern smart homes are being equipped with certain renewable energy resources that can produce their own electric energy. From time to time, these smart homes or microgrids are also capable of supplying energy to other houses, buildings, or energy grid in the time of available self-produced renewable energy. Therefore, researches have been carried out to develop optimal trading strategies, and many recent technologies are also being used in combination with microgrids. One such technology is blockchain, which works over decentralized distributed ledger. In this paper, we develop a blockchain based approach for microgrid energy auction. To make this auction more …

A Reduced Model For Microbial Electrolysis Cells, Dina Aboelela, Moustafa A. Soliman, Ibrahim Ashour

Chemical Engineering

Microbial electrolysis cells (MECs) are breakthrough technology of cheap hydrogen production with high efficiency. In this paper differential-algebraic equation (DAE) model of a MEC with an algebraic constraint on current was studied, simulated and validated by implementing the model on continuous-flow MECs. Then sensitivity analysis for the system was effectuated. Parameters which have the predominating influence on the current density and hydrogen production rate were defined. This sensitivity analysis was utilized in modeling and validation of the batch-cycle of MEC. After that parameters which have less influence on MEC were eliminated and simplified reduced model was obtained and validated. Finally, …

A Reduced Model For Microbial Fuel Cell, Dina Aboelela, Moustafa A. Soliman, Ibrahim Ashour

Chemical Engineering

Microbial fuel cells (MFCs) are a group of microbial electrochemical cells (bioreactors) that are used to generate energy from organic waste found in wastewater. MFCs represent a promising method of waste disposal and production of electricity. Scaling up the use of MFCs requires extensive analysis and detailed grasp of the required processes. The current work aimed to study a model of an MFC, and find the optimum parameters needed for maximum energy production. The process was simulated and validated on continuous-flow MFCs with a Columbic efficiency of 162% and 35% COD removal. Sensitivity analysis of the model was performed. The …

A Half- Yearly E-Newsletter Of The Department Of Computer Science And Engineering, Manipal Institute Of Technology - Jan 2020, Ashalatha Nayak

Faculty work

No abstract provided.

Zechipc: Time Series Interpolation Method Based On Lebesgue Sampling, Luis Miralles-Pechuán, Muhammad Atif Qureshi, Matthieu Bellucci, Brian Mac Namee

Books/Book Chapters

In this paper, we present an interpolation method based on Lebesgue sampling that could help to develop systems based time series more efficiently. Our methods can transmit times series, frequently used in health monitoring, with the same level of accuracy but using much fewer data. Our method is based in Lebesgue sampling, which collects information depending on the values of the signal (e.g. the signal output is sampled when it crosses specific limits). Lebesgue sampling contains additional information about the shape of the signal in-between two sampled points. Using this information would allow generating an interpolated signal closer to the …

Valve Health Identification Using Sensors And Machine Learning Methods, Muhammad Atif Qureshi, Luis Miralles-Pechuán, Wood, Galway Technology Park, Parkmore, Galway, Ireland, Brian Mac Namee

Books/Book Chapters

Predictive maintenance models attempt to identify developing issues with industrial equipment before they become critical. In this paper, we describe both supervised and unsupervised approaches to predictive maintenance for subsea valves in the oil and gas industry. The supervised approach is appropriate for valves for which a long history of operation along with manual assessments of the state of the valves exists, while the unsupervised approach is suitable to address the cold start problem when new valves, for which we do not have an operational history, come online.

For the supervised prediction problem, we attempt to distinguish between healthy and …

Nonhydrostatic Modeling Of Flow Interactions With Highly Flexible Vegetation, Navid Tahvildari, Ramin Familkhalili, Gangfeng Ma

Civil & Environmental Engineering Faculty Publications

Improving our understanding of the interactions between gravity waves, currents, and coastal vegetation, which are nonlinear in nature, enables coastal engineers and managers to better estimate hydrodynamic forces on coastal infrastructure and utilize natural elements to mitigate their impacts. Aquatic vegetation is ubiquitous in coastal waters and it is well-known that flow loses energy over vegetation. Computational modeling of wave-vegetation interaction has been the subject of numerous recent studies and many improvements have been achieved in reducing limitations applied on wave and vegetation behavior in these models. Mechanisms for highly flexible vegetation have been incorporated in a Boussinesq-type model and …

Optimal Feature Selection For Learning-Based Algorithms For Sentiment Classification, Zhaoxia Wang, Zhiping Lin

Research Collection School Of Computing and Information Systems

Sentiment classification is an important branch of cognitive computation—thus the further studies of properties of sentiment analysis is important. Sentiment classification on text data has been an active topic for the last two decades and learning-based methods are very popular and widely used in various applications. For learning-based methods, a lot of enhanced technical strategies have been used to improve the performance of the methods. Feature selection is one of these strategies and it has been studied by many researchers. However, an existing unsolved difficult problem is the choice of a suitable number of features for obtaining the best sentiment …